key: cord-0976651-q3g4rwe0
authors: Al-Azzam, Nosayba; Elsalem, Lina; Gombedza, Farai
title: A cross-sectional study to determine factors affecting dental and medical students’ preference for virtual learning during the COVID-19 outbreak
date: 2020-12-10
journal: Heliyon
DOI: 10.1016/j.heliyon.2020.e05704
sha: ce66f53d7ad349e8f9dd705d7f3822decc8a7fd5
doc_id: 976651
cord_uid: q3g4rwe0

Virtual “online” teaching has been adopted by most universities around the world during the COVID-19 outbreak. This study aims to investigate the factors that might affect students’ preference for virtual learning. Since a second wave of such pandemic is expected to occur, professors and teaching assistants may want to be prepared and aware to create an effective virtual learning environment for students. Using an online survey questionnaire, a total of 488 students in their basic science years of study (first to the third year) who are enrolled in dental and medical college responded to the online survey. The authors utilized a binary logistic regression model to estimate the impact of the nine explanatory variables (gender, student's year of study, accessibility of online tools, class engagement in virtual classes, GPA change during COVID-19 outbreak, class attendance in virtual vs. in-person lectures, type of study material, time saving for virtual classes, and anxiety level during the COVID-19 outbreak) on the students' preference for virtual learning. The analysis of variance showed that three out of the nine variables were not significant to the model: gender, study level, and study material. In addition, to understand the behavioral intention for the students during such pandemic, the online survey questionnaire captured students' voice on their willingness to wear masks, wash their hands, or both as well as their acceptance to take the vaccine once it is available. The results showed that 7.02 % of the students did not change simple health behaviors and 18.43% are not interested in taking the vaccine. This implies the importance of enacting new laws for reopening universities, applying high fines for violators, and obligating students to take the vaccine since university settings have high levels of social contact with populations from different communities and countries.

Using an online survey questionnaire, a total of 488 students in their basic science years of study (first to 19

the third year) who are enrolled in dental and medical college responded to the online survey. The authors 20 utilized a binary logistic regression model to estimate the impact of the nine explanatory variables 21

(gender, student's year of study, accessibility of online tools, class engagement in virtual classes, GPA 22

change during COVID-19 outbreak, class attendance in virtual vs. in-person lectures, type of study 23 material, time saving for virtual classes, and anxiety level during the COVID-19 outbreak) on the 24 students' preference for virtual learning. The analysis of variance showed that three out of the nine 25 variables were not significant to the model: gender, study level, and study material. In addition, to 26 understand the behavioral intention for the students during such pandemic, the online survey 27

questionnaire captured students' voice on their willingness to wear masks, wash their hands, or both as 28 well as their acceptance to take the vaccine once it is available. The results showed that 7.02 % of the 29 students did not change simple health behaviors and 18.43% are not interested in taking the vaccine. This 30

implies the importance of enacting new laws for reopening universities, applying high fines for violators, 31

and obligating students to take the vaccine since university settings have high levels of social contact with 32 populations from different communities and countries. 33

Although older people are among those at a higher risk for severe illness from coronavirus disease 19 38 , young adults can be infected and can transmit the virus to others (1, 2). In fact, during a 39 pandemic, young adults are more likely to be disease-ridden and asymptomatic, which increases the 40 possibility of university campuses becoming hot spots for disease spreading (3). A previous study has 41 explored the perception of invincibility in young adults with many young adults believing that they will 42 not be affected by disease; this is particularly worrying in the case of COVID-19 as asymptomatic virus 43 carriers can become the catalyst for community spread (4). Taking precautionary action is essential to 44 minimize the disease influence in the institution as well as the surrounding community (3). 45

The global COVID-19 pandemic emanating from Wuhan, China has ravaged the world. As of September 46 2020, more than 28 million cases of COVID-19 and over 900, 000 deaths have been recorded worldwide. 47

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and presents 48

variably from asymptomatic infection to as a severe pneumonia-induced death (5). SARS-CoV-2 is 49 highly contagious, which enforces the need for social and physical distancing measures to slowdown the 50 spread of the disease (6). Moreover, it is imperative to examine the effects of COVID-19 on higher 51 education in general and on dental and medical students-future healthcare practitioners in particular . On 52

March 2 nd , 2020, the Ministry of Health confirmed the first case of COVID-19 in Jordan. Even though the 53 country only had one confirmed case, on March 14 th , the government mandated social restrictions 54

including suspending school, banning large gatherings and restricting local and international travel to 55 ensure the safety of the nation against the ensuing pandemic. Around the world, university campuses 56

were closed mid-to late March 2020, and face to face/in-person instruction was disrupted. Because of the 57 rapid nature of disease onset and spread, many institutions were caught off-guard and were unprepared for 58 the switch to online learning (7). In many cases plans for virtual learning were cobbled together 59

overnight. The hasty transition, a general lack of preparedness, and bandwidth led to an unfulfilling 60 virtual learning experience for both instructors and students (8, 9) . 61

While it is difficult to differentiate the efficacy of instruction delivery formats on a whole scale across 62 disciplines and institutions, general perception regards virtual learning as being of lesser quality compared 63

to face-to-face instruction. However, emerging work shows that there is no significant difference among 64 delivery formats including face-to-face, blended, and virtual instruction (10-12). For example, a study 65 among medical students from Saudi Arabia revealed that the online teaching is a well-received modality 66 that has many advantages including time saving and a better students' performance (13). However, the 67 rapid transition of many institutions towards virtual teaching masks the perception of virtual learning as a 68 low quality choice, though, under these circumstances the transition did not take full advantage of the 69 affordances and possibilities of the online format (11 Compared to face-to-face instruction, students lamented the loss of collegial atmosphere. Indeed, peer to 101 peer teaching and collaborative work is vital to the development of well-rounded students (27). 102

We conducted this survey to assess factors that might affect students' preference for virtual learning 103 during this disease outbreak including gender, student's year of study, accessibility of online tools, 104 student engagement invirtual classes, GPA change during COVID-19 outbreak, class attendance in virtual 105

vs. in-person lectures, type of study material, time saving for virtual classes, and anxiety level during 106 COVID-19 outbreak. Further, our study aims to better understand the perceptions and behaviors of 107 students during a general global pandemic. This informational is critical for administrators and decision 108 makers at higher education institutions to gather input from students' experiences on the effect of the 109 COVID-19 pandemic on student learning and prepare students for a possible second wave later in 2020.

Significantly, administrators, parents, and students all need to be well informed and clear in this regard as 111 they prepare for either campus reopening or an extended hiatus from campus. 112 113

This study aims to examine the following research questions: 115 1. Is it feasible to predict the dental and medical students' preference for virtual learning of basic 116 science courses (first to the third year) through the following main variables: 117 a) Gender b) student's year of study (first, second, or third year) c) accessibility of online tools 118 d) class engagement e) GPA change during COVID-19 outbreak f) type of study material 119 (e.g., book, recorded videos, PowerPoint) g) class attendance h) time saving i) anxiety level 120

during COVID-19 outbreak 121

2. What are the significant variables (which are listed in the question 1) that influence students' 122 preference for virtual learning? 123

3. How prepared arestudents in the event of a second wave? 124 125 J o u r n a l P r e -p r o o f

The minimum number of sample size which is required for this study was determined based on three 128

factors: 129 1) students' population size 130

2) margin of error which was set to be ±5% which is an acceptable value for categorical variables (28) 131 3) confidence level that was set to be 95% for this design experiment (28) (29) (30) . 132

The total number of dental and medical students in their first to third year of study who were enrolled in 133 the spring of 2019/2020 academic year is as in Table 1 . 134 135 137 Based on the above information and using the following equation, the minimum number of sample size is 138 347 surveys (29, 31, 32). 139

Where S is the sample size, N is the population, and 141

Where Z is the critical value of normal distribution (1.96 is the value for 95% confidence, p is the sample 143 proportion (0.5), and MOE is the margin of error 144 145

In general, missing data in the surveys can be produced for many reasons. For example, some of the 147 participants may skip answering some questions intentionally due to privacy issues, stress, or lack of 148 knowledge. Other reasons might be that insufficient time that was given to the respondent to complete the 149 questionnaire or the survey was too long which requires a greater amount of time to be completed which 150

drives the respondent to lose interest (33-35). However, our surveyconsisted of only twelve multiple 151 choice questions. Every question in the survey without an answer is considered as a missing data point.

Missing data can be deceiving since it is hard to identify the problem. Besides, it is not always 154 understandable when missing data can be a serious problem. For instance, each variable or question in the 155 survey may only have a small number of missing responses, but grouping all of these missing responses 156

in the survey could result in numerous total missing points (36, 37). Therefore, it is essential to assess the 157 J o u r n a l P r e -p r o o f missing points to successfully manage the survey data and avoid any type of inaccurate interpretation 158 regarding the data. 159 160

Handling Missing Values 161

Missing data is considered as a rule rather than an exception in quantitative studies (38). A missing 162

proportion of 15%-20% is usually common in psychological and educational research. Experts have not 163 determined a cutoff point for the percentage of missing data that turn out to be problematic. Schafer 164 recommended a cutoff point of 5% (39). Bennet suggested that if 10% of data is missing, statistical 165

analyses are more likely to be misleading (40), however other studies have used data that has 20% 166 missing points (41). Of note, the nonresponse rate in our survey is 1% of the total sample (n=5), therefore 167 the amount of bias will be very small. Hence, dropping or omitting those records from the analysis is a 168 reasonable approach (42).

Survey Design and Implementation 171

A panel of four experts in educational technology and science or medical fields evaluated the items in the 172 survey to assess the content validity. The questionnaire was piloted with 25 students and feedback 173 responses were collected regarding the clarity and validity of questions. 174

The final version of the questionnaire was composed of twelve multiple choice questions. The 175

questionnaire was created using Google forms and the link for this form was distributed among medical 176

and dental students in their basic years of study (the first to third years) at Jordan University of Science 177

and Technology (JUST). No personal or identifying information was sought. The survey was anonymous, 178

neither e-mail nor IP addresses were recorded for any respondent and responses were saved in PI's 179

Google account. Ethics approval was granted by the Institutional Review Board (IRB# 13/134/2020) at 180

University of Science and Technology. The questionnaire captured students' voice on the following 181 variables which are shown in Table 2 . 182

The questionnaire was available from June 20, 2020 to July 25, 2020. The collected information was 183 recoded as illustrated in Table 2 and then entered into a database. 184 regression , also referred to as the logistic model or logit model, analyzes the relationship between 195 multiple independent variables and a categorical dependent variable and estimates the probability of 196 occurrence of an event by fitting data to a logistic curve (44) 197 Logistic regression is a flexible approach and independent of the relationship between input and target 198

variables. The main benefit of using logistic regression is its capability of determining the proportional or 199

inversely proportional relationship between the input and target variable. There are two types of logistic 200 regression: the binary, where the target variable is binary, and multi-class, where the target variable has 201 more than two categories. 202

In this study, a binary logistic regression model was used to describe the relationship between a binary 203 response categorical variable (Preferred learning method) and a set of explanatory variables (Gender, 204

Student's Year of Study, Accessibility online tools, Class Engagement in the virtual classes, GPA Change 205 during the COVID-19 outbreak, Class attendance in virtual vs. in-person lectures, Type of study material, 206

Time saving for virtual classes, Anxiety level during the COVID-19 outbreak). In addition, a descriptive 207 analysis for students' behavioral adaptation and their willingness to take the vaccine when it is available 208 was performed. A schematic diagram for developing a binary logistic model for this study is shown in 209 Figure 1 . 

A total of 488 students agreed to participate in the study. Most of the participants were female students 217 (57.58%). Almost two-thirds of the respondents (64.33%) were first-year students, and the other third 218

were second-and third-year students. The detailed characteristics of participants are shown in Table 3 . 219 220 Step 1: Recoding of the Data

Step 2: Determine the significant variables that have an influence on the Response Variable depending on the P value ( P less than 0.05)

Step 3: Applying a binary logistic regression analysis for significant variables using forwardstep procedure

Step 4: Select best binary logistic model J o u r n a l P r e -p r o o f

First, students' voices regarding their preference for virtual versus in person learning was assessed. Most 224 of the students (67%) preferred in person over virtual leaning. However, only 32% of students preferred 225 virtual learning in comparison to in campus (data is not shown). 226

Then, to determine the variables that significantly influence the students' preference for virtual learning, a 227 binary logistic regression analysis was carried out to examine all the defined explanatory variables Table  228 4. The analysis of variance showed that three out of the nine variables were not significant to the model as 229

shown in the Figure 2) , which indicates that the model can be used to predict students' preference for virtual 241 learning. The estimate of the regression coefficients (Coef) ( Table 5 ) was used to formulate the logistic regression 247

equation (1). The standard error of the coefficient indicates the precision of the estimate of the coefficient. 248

The smaller the value, the more accurate estimate. In addition, the coefficient p-values are calculated 249 based on the Wald tests. Both the class engagement and class attendance have the smallest p-values 250 ( 0.00001), which indicate that both are the closest significant predictors for students' preference for 251 virtual learning. 252 253 257

A coefficient value indicates the extent to which a particular explanatory variable contributes to the 259 possibility of the response variable to be virtual learning. For example, when the student attends more 260 online classes than in person classes (Class attendance in virtual vs. in-person lectures _2), the logit 261 transformation of preferring virtual learning increases by 2.069. However, when the student access to 262 online tools is hard (Ease of accessing online tools_3), the logit transformation of preferring virtual 263 learning decreases by 1.928. As a summary from table 5, factors that positively enhance students' 264 preference towards virtual learning is easy access to online tools, class engagement, GPA increase, 265

increased attendance, no anxiety during the pandemic, and time saving. On the other side, the only factor 266 that negatively affected students' preference for virtual learning was the difficulty of accessing to online 267 tools. 268

To understand the effect of the explanatory variables in the model, we used the odd ratios for the 270 categorical predictors as illustrated in Table 6 . Since the predictors in this study are categorical, the event 271

(method of learning) is compared at two different levels for each predictor. When the odds ratio is higher 272 than 1, that indicates the event is more likely to occur when the predictor is at level A. When it is less than 273 1, this indicates that the event is likely to occur at level A. For example, the odds ratio for student 274 engagement in the virtual classes' variable is 4.0573. This indicates that the odds that a student prefers 275 virtual learning is 4.0573 times higher for a student who feels more engaged in virtual courses. On the 276 other hand, the odds ratio for ease of accessing online tools variable is 0.0800. This indicates that the odds 277 that a student prefers virtual learning is 0.0800 times less likely for a student who has a difficult time 278

accessing online tools compared to a student who can easily access them. 279 280 281 282 engaging for the students, his/ her GPA is increased during the pandemic, does not feel anxious during the 288 pandemic, and saves time by taking online courses. The ease of online access influences students' 289

preference but not as much as the other mentioned factors. 290

On the other hand, to understand the behavioral intention for the students during this pandemic, we 292

mainly focused on capturing their voices on their willingness to wear masks, wash their hands, or both as 293 well as their acceptance to take the vaccine once it is available ( Table 7) . Most of the students (69.21%) 294 are washing their hands more often and wearing masks as well. 13.43% are washing hands only and 295

10.33% are just wearing masks. However, 7.02 % of the students did not change their simple health 296

behaviors. This indicates the importance of enacting new laws for opening the universities and applying 297 high fines for violators which might render this 7.02% of student population from violating the rules. On 298 the other hand, more than three fourths (81.57%) of the students are interested in taking the vaccine and 299

around one fifth of them are not interested. This might reveal the importance of implementing virtual 300 health education programs that could be arranged by universities to increase students' awareness in this 301 regard, since universities have crowding, and often host people from different communities and even 302 different countries. 303 Toulouse, France admitted that they learned more about virtual/distance learning in two months of 319

COVID-19 crises than in the ten years prior. This is mainly attributed to their obligation and devotion to 320 education during the emergence of the pandemic (49). However, challenges such as difficulty in ensuring 321 quality of student assessment and limited student engagement remain as problems (50). Van Doren et al. 322 found that virtual dental learning has been a primary approach to continue the education process during 323 the pandemic but there were some limitations in presenting preclinical and clinical education. The study 324 suggested using videos, virtual cases, and recorded lectures to improve the quality of virtual learning in 325 dentistry (51). Further, Salter et. al. showed that virtual learning worked efficiently in pharmacy education 326

and increased the knowledge content of the students, however, it was difficult to assess its effect on the 327 skills or professional practice of pharmacists (12). Notably, clinicians and trainees were also affected such 328 as surgeon or dental trainers and their education (52-55). 329

Many factors affect students' preference towards a particular learning method during a global crisis of 330 epic proportions. In the present study, we examined the effect of factors that affected students' preference 331

for virtual learning. Comparable to the study by Paul et. al. (56) gender was not a significant factor that 332 influence students' preference towards virtual learning. Also, in our study, the study year level of the 333 students and the type of study material were not significant factors on students' preference. 334

Our results indicated that the students' attendance to virtual classes, class engagement, and anxiety level 335

had an influence on students' preference for virtual learning. Upon shifting to virtual learning, increased 336 burnout, decreased engagement, and the same perception level of class attendance were reported by 337

Dental Medicine students at Harvard School (15). In addition, a recent study among medical and dental 338 students at Liaquat College reported that 77% of students have negative perceptions towards e-learning 339

and they did not prefer virtual learning over the face-to-face modality during the lock down situation.

84% of these students reported a limited student-instructor interaction (57). This might be an indicator for 341 academic instructors to include more interactive material to accommodate virtual classes such as 342 discussion forums, uploading study cases and videos, and constructing regular ungraded short quizzes 343

which altogether tend to increase attendance, enhance student engagement with class contents, and 344 decrease stress. Furthermore, students can reduce their stress and anxiety levels by changing their 345 lifestyles such as practicing exercises or planting a garden which might reduce their stress and 346

consequently increase their focus on their studies.

Time saving was also an important factor that affects students' preference for virtual learning. A previous 349 study also reported that virtual learning sessions save students' time and improved their academic 350 performance due to enhanced utility of time (13). Further, GPA change was a significant factor in 351 determining students' preference for virtual learning. This suggests instructors fulfill a role to ensure that 352 the student assessments are as accurate reflection of their aptitude, that is, the virtual platform does not 353 hinder their performance of over or underestimate their achievement.

In line with other studies, we found that difficulty accessing online tools was a hindrance affecting virtual 355

learning (13, 58) . This suggests that universities should facilitate access to online tools by supporting 356

technologies and offering trainings to increase user competency. Crucially, enhancing accessibility to 357

online tools for all students would address equity and guarantee a fair environment for each student to 358 have access to their classes. 359

Notably, most respondents indicated that they practiced basic health precautionary measures, yet our 360 study revealed that only 7.02 % of the students did not change their simple health behavior by wearing 361 masks or washing hands. Universities could capitalize on these positive behaviors by emphasizing other 362

global WHO guidelines such as social distancing to restrict the spread of the disease. Of note, our study 363 also showed that 18.43% of respondents were not interested in taking the vaccine. In the event that a 364 vaccine does become available, administrators may need to be aware of pushback and provide clear 365 guidelines on policing vaccine uptake. Significantly, this also implies that new laws or ordinances may 366 need to be enacted to facilitate safe reopening of universities. 367

Our study possesses a limitation due to reliance on a survey conducted in JUST and thus may not be 369 generalizable to other institutions worldwide. Our study found that accessibility of online tools, class 370 engagement in the virtual classes, GPA change during COVID-19 outbreak, class attendance, and anxiety 371 level during COVID-19 outbreak are significant factors that affect students' preference for virtual 372

learning. In addition, the results showed that 7.02 % of the students did not change simple health 373 behaviors and 18.43% were not interested in taking the vaccine. This implies the importance of enacting 374 new laws for reopening universities while convincing the students to take the vaccine. 375

Our findings may be beneficial to academic administrators, instructors, and institutions in implementing 376

programmatic tactics to improve effectiveness of virtual learning and increase their readiness for schools 377

reopening. Future studies that analyze more parameters of each variable by asking open-ended questions 378

or interviewing the students can be helpful and better evaluate the effect of the above determined 379 significant parameters and to follow up on our findings. Also, it would be interesting to compare students' 380 preferences for virtual classes versus face-to-face classes in terms of GPA and academic performance in a 381 more detailed manner. In addition, future studies that evaluate the students' performance after the 382 pandemic have to be explored with special attention toward determining the factors that affect students' 383 preference toward online/distance exam evaluations and determining the factors that affect the clinical 384 experience and knowledge of medical and dental students during the COVID-19 outbreak. Thanks for all the students who participated in this study. Special thanks to Cameron La Belle and 402

Kathleen Aton for editing the manuscript. 

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