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Article

Perspectives on the Impact of E-Learning Pre- and Post-COVID-19 Pandemic—The Case of the Kurdistan Region of Iraq

by
Dler O. Hasan
1,
Aso M. Aladdin
1,2,*,
Azad Arif Hama Amin
3,
Tarik A. Rashid
4,
Yossra H. Ali
5,
Mahmood Al-Bahri
6,
Jafar Majidpour
7,
Ioan Batrancea
8,* and
Ema Speranta Masca
9
1
Department of Computer Science, College of Science, Charmo University, Chamchamal 46023, Iraq
2
Department of Technical Information Systems Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil 44001, Iraq
3
Department of Financial Accounting and Auditing, College of Commerce, University of Sulaimani, Sulaymaniyah 46001, Iraq
4
Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil 44001, Iraq
5
Computer Science Department, University of Technology, Baghdad 10011, Iraq
6
Faculty of Computing and IT, Sohar University, Sohar 311, Oman
7
Computer Science Department, University of Raparin, Rania 46012, Iraq
8
Faculty of Economics and Business Administration, Babes-Bolyai University, 400591 Cluj-Napoca, Romania
9
Faculty of Economics and Law, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology, 540142 Targu Mures, Romania
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(5), 4400; https://doi.org/10.3390/su15054400
Submission received: 16 January 2023 / Revised: 23 February 2023 / Accepted: 25 February 2023 / Published: 1 March 2023
(This article belongs to the Special Issue SMEs and EU Regional Development)
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Versions Notes

Abstract

:
The COVID-19 pandemic profoundly affected global patterns, and the period of the declared virus pandemic has had a negative influence on all aspects of life. This research focuses on categorizing and empirically investigating the role of digital platforms in learning and business processes during the COVID-19 pandemic outbreak. The main purpose of this paper is to investigate to what extent the use of electronic learning (EL) has been boosted by COVID-19’s spread, and EL’s effectiveness on the sustainable development of electronic commerce due to the demand for a variety of electronic devices. For this purpose, the information has been collected through an online questionnaire applied to 430 participants from the Kurdistan Region of Iraq (KRI). The results indicate that participant usage and skills with electronic devices and online software programs are increasing, as the ratio indicated a level of 68% for both genders. Thus, the significance of EL concerning electronic commercial enterprises has been openly acknowledged and influenced by numerous factors. In addition, several suggestions and steps to be undertaken by the government are highlighted. Finally, this research mentions the current limitations of EL and suggests future works to build sustainable online experiences.

1. Introduction

The outbreak of COVID-19 has had a revolutionary impact on EL [1,2,3]. The use of online tools has been adopted increasingly to deliver lectures and materials studied in schools and universities during lockdowns, self-isolation, and social distancing in the community. Despite several wide-ranging influences of online platforms, this pandemic has triggered changes in finding the best methods of education to connect instructors with students [4,5]. Thus, COVID-19 has impacted EL around the world and has changed the nature of business, especially with regard to the high demand for technological devices [6]. Globally, EL has been significantly impacted by COVID-19, sometimes negatively [7], as some types of EL, such as blended learning, could not be implemented due to complete lockdowns. On the other hand, student motivation was affected by a lack of social interaction. Additionally, spending more time in front of a computer screen disrupted students’ sleep, which can cause a number of negative and unhealthy behaviors. Overall, EL has expanded quickly due to the dramatic spread of the virus. Users have been forced to utilize the internet and incorporate it into their everyday routines due to the coronavirus and its variants [8,9]. The COVID-19 epidemic has pointedly altered consumer preferences, not only in the KRI but also internationally.
As almost 5 million residents of the KRI were in self-isolation, a steady decline was also noted in activities included in the cycle of learning in schools and universities. EL is a crucial replacement to address the issue of declining learning activities. The government (KRI) has ordered all the state and private schools to stop offering on-campus instruction and switch to distance learning using synchronous and asynchronous online learning systems. As a result, online instruction became a common practice, yet it has significant drawbacks. Due to societal inequalities that exist in several countries, not all students have access to this means of education appropriate to the circumstances [10,11,12]. Unusual events during the lockdown period have affected the entire nation and its inhabitants. During lockdowns, a political management that is effective and capable of handling every component of daily life is needed due to concerns for a new economic recession [13]. Economy and environmental health are important topics in each state, but so too is the delivery of education; EL changed and grew during the pandemic [14]. Thus, socio-demographic characteristics, governmental decisions, and commercial factors have all had several different effects. These potential factors can be used to measure the use of new technology in specific regions. Researchers, therefore, need to carefully investigate particular regions in the world. Similar to other nations, the KRI managed lockdown and quarantine by restricting social connections in the fields of education, business, and healthcare.
These changes are such that the educational sectors have resorted to providing their services online, which undoubtedly has several positive and negative consequences. Researchers have begun to examine the consequences and impacts of the pandemic on people’s life worldwide since the outbreak of the coronavirus [15]. Online activities in different aspects of life have rapidly increased in terms of commerce and education. The importance of telework has increased as in-person shopping shifted to online shopping, and video connections and conferencing have increasingly been used since the appearance of COVID-19 [16]. Thus, adaptation to new circumstances is a key to success, especially for those experiencing new circumstances that emerged after the pandemic. According to one study, Albanian students who attended online classes for the first time encountered a lot of challenges and dissatisfaction [17]. In another study [18], it is evidenced that there was a change in the range of online activities people undertook because of the coronavirus. For example, shopping online has replaced participation in the markets for purchasing and selling goods and services. Based on a survey in Greece, it was shown that both the importance of EL and the frequency of engaging in EL had significantly increased during COVID-19 as compared to the time before the appearance of the virus. Accordingly, these changes have influenced electronic commerce (EC) and the behavior of consumers. People’s shopping behaviors have changed to online shopping [19,20]. [19] stated that COVID-19 has left a negative impact on the workforce for in-person markets, while EL has increased. Today, people are increasingly shopping online, as the behavior of consumers has been differently shaped as a result of emergent circumstances [21]. The pandemic forced consumers to utilize online payment methods to buy products and services. Similarly, during the pandemic, education had to be delivered virtually, and many businesses and educational institutions successfully made the transition in a rather short period, which has had a great impact on the growth of EL [22,23].
Infrastructure development has been accelerating swiftly in the KRI. After COVID-19’s global outbreak, this area experienced the same commercial and educational development challenges [24,25]. There are several objectives behind improving and analyzing the data for this research paper. The overall main objective is to investigate to what level the use of EL has been boosted during and after the spread of COVID-19, and the impact on the long-term growth of EC triggered by the use of different electronic devices needed to access EL. Several researchers have evaluated the influence of electronic device consumption and have highlighted the situations all over the region [26,27]. Thus, similar ideas assert that the students’ view and approach to educational technology has a direct impact on both their learning process and the use of the electronic market [28].
Recent studies have reported that the key determinants of commercial adoption of EL during the pandemic are technical enablers, such as the simplicity with which an EL platform can be used, users’ knowledge about the advantages of EL, adequate security, and accessibility of internet resources. This can be seen particularly in learning technology systems, which have the greatest influence on delivery time [29,30,31]. The factors of employment status and socio-demographics of those using EL impact the choice of device types to use for online learning [32]. As a result, two separate motivations support the original objective of this research. The first motivation is to focus on the influences of the pandemic on EL, such as learning or schooling flexibility and a decrease in face-to-face connections between people. The second motivation is to illustrate the effect on the choice of electronic devices, especially in the KRI, according to the reactions of participants.
The main contributions of this research article are classified into several areas. The first is the variation in user ratios before and after the pandemic outbreak. A large proportion of respondents are unwilling to utilize EL for various purposes. The second is governmental support, which refers to the assistance provided by the government to encourage the spread of technological innovations after the COVID-19 outbreak. In addition, the government’s primary role is to develop the EL process by expanding resources and technology, and supporting facilities while balancing the influence of commercial development on the EL process. The third contribution indicates that the demand for electronic devices and internet services has increased after the COVID-19 epidemic. As a result, the price of electronic devices has rocketed, and the impact of the EL process on EC has been evident.
The additional findings of this research are given below. The next part is devoted to demonstrating the methodology and materiality of data obtained from respondents, as well as the statistical approach suggested to evaluate the data in the next section. The third section focuses on data analysis. The primary discussion of results is covered in the fourth part. The fifth section presents future work and the limitations of this study. Finally, the last section covers the study’s limitations and the authors’ conclusions.

2. The Effect of Electronic Learning on Electronic Commerce

In EC, tangible products are more important, while services (such as distance learning) are rarely included in people’s decisions to purchase. Conversely, in EL, a higher focus is placed on knowledge services. Since both EL and EC are services delivered online and they use electronic techniques and devices, the growth in one can highly be attributed to the accelerating growth in the other. Furthermore, according to [33], EL is considered a special type of EC since EL services are provided online, are purchased and sold online through EC websites. Because of the previously stated reasons, the dramatic increase in EL usage after the COVID-19 pandemic began was one of the major reasons for the rise in EC [34]. Therefore, there are two main ways in which EL has led to a surge in EC after the pandemic. Firstly, the rise of EL during and after the pandemic has increased the demand for electronic devices, thus fueling the rapid growth of EC. Secondly, wide and intensive use of EL has determined customers to acquire the ability to manage their EC activities easily and also to gain a better understanding of the terms and practices involved in EC.

3. Methodology

3.1. Study Design and Participants

To collect data from different kinds of people of various ages, employment statuses, and qualification levels, an online survey was conducted among the general population of the KRI over 90 days from 4 April 2022 to 2 July 2022, in which 430 people participated. This kind of survey has gained popularity among researchers due to its ease of administration, inexpensive cost of dissemination, and speed of analysis [35]. This survey was developed using Google Forms, and the link was then shared via email, Facebook Messenger, WhatsApp, Viber, and other messaging applications. The survey link was also sent by the researchers with a request for shares and distribution among other networks. This study’s purpose was clearly explained to the respondents through the study description form, and the respondents were also informed that the survey was anonymous. The survey questionnaire was available in three languages (Kurdish, English, and Arabic) because some foreign students and employees study and work in Kurdistan universities or companies, and their native languages are not Kurdish.

3.2. Survey Classification Factors

The survey questionnaire consisted of three main sections comprising 20 questions to gather data about three major factors. The first section collected demographic information, which includes items on participants’ gender, age, employment status, and education degree completed. Each variable was split into groups, as can be seen in Table 1. For example, the age variable was classified into the seven age groups of 12–17, 18–24, 25–34, 35–44, 45–54, 55–64, and 65 and older.
The second major section is used to show the impact of COVID-19 on EL and it included two similar groups of five items. The first five questions in the set were about using EL before the COVID-19 outbreak started and the second set was about using EL after the COVID-19 outbreak started. The respondents were able to answer the questions in a set only if they had used EL during that period. The five questions in each set of this section asked about participants’ use of the most popular EL methods, their most preferred electronic device for EL, the scope of EL methods they have utilized, the period of using EL as an instructional platform, and the type of software applications they have mostly used. Moreover, each of the questions was categorized into multiple groups, as demonstrated in Table 1.
The last section asked respondents three questions to indicate their skill level in using an electronic device, to what extent they have agreed to substitute traditional classes with the EL classes, and the most important features of EL they have utilized.

3.3. Data Analysis Techniques and Statistical Methods

The data were statistically analyzed using Python 3.9.7 programming and Anaconda 4.10.3 (Jupyter Notebook 6.4.5). Also, several Python data analysis libraries were used, such as Pandas for data analysis and manipulation, SciPy for performing the chi-square tests and the Mann–Whitney U test, Pingouin for performing the Kruskal–Wallis test, and Matplotlib and Seaborn for graphing and visualization, respectively.
For descriptive statistics, proportions and percentages were used to describe categories of data. Also, this approach might select a statistical test to analyze deterministic data. In the descriptive analysis, instead of parametric tests (such as ANOVA and t-tests), non-parametric statistical tests were used, since all of our data are categorical (gender, age group, level of education, employment status) [36]. Where the dependent variable is nominal (such as using/not using), the chi-square test was used. This test determines whether there is a significant association between two categorical variables by determining if a categorical variable’s observed frequency distribution differs significantly from its expected frequency distribution. Furthermore, the Mann–Whitney U test [37] was only used when our dependent variable was ordinal (such as very slightly efficient, slightly efficient, somewhat efficient, very efficient, and extremely efficient) and the independent variable has only two groups (such as gender male/female), which is also known as a dichotomous variable. In cases where the dependent variable is ordinal and the independent variable has more than two groups (such as employment status), the Kruskal–Wallis test was conducted [38], as determined in the analyzed table in Section 4. Therefore, for more mathematical improvement, the chi-square test was used to analyze and investigate the relationships between socio-demographic factors and the use and non-use of EL before and after the COVID-19 pandemic. Furthermore, The Mann–Whitney U test, and the Kruskal–Wallis test were used to test whether there are differences across levels of socio-demographic factors regarding the preference to replace traditional classes with EL classes.
In statistics, a categorical variable has a small, usually fixed range of possible values and categorizes [39]. In addition to the matrix itself, we obtained a cross tab χ 2 with a chi-square test of independence [40] as specified in the first formula [41], which emphasizes the significance of the correlation between variables.
x 2 = ∑ i = 1 c ∑ j = 1 r O i j − E i j 2 E i j
However, all of the variations use the same idea, namely that outcomes are a comparison of the expected values and the values collected. Where i is a point in the situational N sample table (which includes the row r and column c ), O is the observed value, and E is the expected value.

4. Results and Analyses

4.1. Results of Socio-Demographic Variables

Researchers use socio-demographic standards to target factors and categorize their audience into different subgroups, especially in EL research. Table 1 and Figure 1 show the overall samples of 430 participants, with all living in the KRI. The data were classified into two main groups: the first showed significantly the proportion of respondents who used or not EL and applications devoted to EL before the COVID-19 outbreak, and the second group has been further divided into subgroups depicting the same behavior but for the period after the COVID-19 outbreak. In the first group, the rates of demand for EL and its applications were low before the COVID-19 outbreak. Approximately 35% of participants used EL and its applications; however, the proportion of participants who were non-users was high (65%). On the other hand, after the COVID-19 outbreak, the demand for using EL and related applications has increased considerably. Thus, the result has shown that almost 68% of participants have used EL and its applications. Despite the demand for devices, a high fraction of this demand is due to the focus of online-based services. The outcome is illustrated in Figure 2.
There were slightly more male participants than female participants. Therefore, this difference has a little bit of impact on the research, because utilizing online facilities in EL procedures both before and after the COVID-19 outbreak provides almost the same outcomes for both genders. As a consequence, the data reveal that participants utilized the EL process before the spread of the virus in about 37% of men and 32% of women, but that it has been used after the virus’s outbreak by around 69% of men and 66% of women. According to this outcome, the possibility of automating distance learning procedures as a new technological innovation makes the traditional distance education business model more efficient. As a result, it offers free online learning opportunities for consumers to create connections while educating clients on how to operate the product online. Furthermore, it supports extending the market range of services and includes online learning, which impacts commercial implementation by teaching employees about new EL systems and creating demand for technological devices [42,43].
The willingness of respondents to participate in the learning process is significantly influenced by their age group. The results show that there are no responders over the age of 54. Most respondents were between the ages of 35 and 44. Furthermore, this group revealed that the majority used the EL process after the COVID-19 outbreak. The percentage has risen sharply from 44.4% to 71.8%. This value, on the other hand, has dropped dramatically for participants who did not use EL in this age group (the ratio has changed from 55.6% to 28.2%). Likewise, all age groups show the same outcomes regarding the impact of the pandemic. The same result has been shown for the degree of education category, which has been grouped into subgroups; this result also illustrates the demand for EL after the COVID-19 outbreak. The influence of COVID-19 has been especially noticeable in participants with bachelor’s or master’s degrees, as well as those who are in the process of receiving these degrees. The percentage results for participants with a bachelor’s or master’s degree who used EL after the disease outbreak are 61.6% and 85.6%, respectively); however, the ratio before COVID-19 is lower for both groups (28.9% and 45.2%, respectively). Further, the disease has shown the same impact in the subgroups of employment status characteristics, particularly on students and teachers, because these two subgroups account for the majority of participants who used this feature. The ratio of student participants who have used electronic processes for learning increased from 26.6% to 61.0% and dropped for non-users to 39.0; furthermore, a similar alteration in the ratio for teacher or instructor participation, from 46.1% to 78.7, has been shown due to the impact of pandemic. These outcomes are illustrated in Figure 1.
The methodology section specifies that the collected data have been evaluated by the crosstab model as a statistical procedure. According to the model, the summary of which is displayed in Table 1, the crosstab result for gender has dropped after the COVID-19 outbreak. Hence, the p-value is lower, but there is no statistical significance since the null hypothesis is accepted since the p-value exceeds 0.05 for gender following the COVID-19 outbreak. Conversely, age, degree of education, and employment status have statistical significance because of the corresponding p-values (age, p = 0.005; degree of education p = 10 × 10 − 7 ; employment status p = 0.015) after the COVID-19 outbreak. Regarding the pre-COVID-19 period, the crosstab results are significant only for degree of education ( p = 0.0014) and employment status ( p = 0.006).

4.2. Effect of COVID-19 on Electronic Learning

EL is a term used to describe the delivery of education electronically. As shown in Table 2, we have divided our data between two periods of time, before and after the outbreak of coronavirus. It is obvious that the most popular method used before the outbreak was synchronous online learning (32.5%) as compared to the other methods; it is very clear that there is a sharp increase (36.0%) in the use of this method during the outbreak.
In this research, we asked about the platforms that have been used for online education. Our findings indicate a small variation in smartphone use for learning both before and after the coronavirus outbreak. Table 2 shows that 31.5% of participants (i.e., 151) used a smartphone before the outbreak, and 39.4% used a smartphone after the coronavirus pandemic, but in both periods the use of laptops is very high compared to smartphones. It is obvious that most electronic devices that are used to access the internet are also employed for EL, and this is an easy-to-grow trade branch.
Our data show that before and after the appearance of the coronavirus, the use of EL as a method of learning was favored by the top levels of education (49.0% before COVID-19 and 42.5% after) as compared to the other options shown in Table 2. But if we compare both periods for the same variable, we can see that, after the appearance of coronavirus, there is a slight decrease in the use of EL methods as before the outbreak of coronavirus. This is due to the increase in demand from 19.9% to 25.0% on the use of other EL methods, especially for online conferencing.
To run an EL platform, it is obvious that different types of software are required. For that reason, survey participant indicated that the highest number of people (34.7%) used a learning management system (LMS) such as Moodle, Google Classroom, Edmodo before the pandemic, but during the pandemic, the number of participants using the different software fluctuated. As shown in Table 3, the number of people who have used web conferencing and webinar tools such as Zoom Meetings or Google Meet after the pandemic outbreak is very high (33.6%) as compared to before the pandemic, which is (26.1%). In this question, the participants are allowed to select more than one response.

4.3. Analysis of the Statistical Relationship between Personal Employment Status and Basic E-Learning Features after the COVID-19 Pandemic

In Table 4 the respondents are distributed according to employment status. This is to show the EL method, the level of EL method, and the type of electronic devices that were commonly used and preferred by students, teachers, employed individuals, and unemployed individuals during and after the pandemic. Moreover, we examine how long the individuals used EL after the pandemic. The results in Table 4 indicate statistically significant relationships between employment status and the most preferred device for EL, the scope of the EL method, and the duration of using EL as an instructional platform. But there was no significant relationship between employment status and the most popular EL method used. Smartphones and laptops were both popular among students (47.2% preferred smartphones and 43.5% preferred laptops), employed (44.0% preferred smartphones and 50.0% preferred laptops), and unemployed (57.1% preferred smartphones and 42.9% preferred laptops) respondents for online learning. In contrast, the majority of teachers (68.5%) indicated that laptops were the most suitable devices for online education since laptops are easier to navigate with and for typing. The scope of the EL method utilized by most of the students was for general education (47.2%) while teachers utilized two EL methods equally, which were general education (43.2%) and conferences, seminars, workshops, or symposiums (42.3%). Table 4 also shows that more than 30% of respondents used EL up until the survey was conducted, which was more than two years after the COVID-19 pandemic started.

4.4. The Impact of Electronic Learning on Adaptive Skills

Participants were asked to classify their skills in using electronic devices into five major categories: poor, insufficient, moderate, sufficient, and exceptional. Table 5 indicates that a small percentage of participants estimated their electronic device skills as poor (2.6%) or insufficient (5.6%). Meanwhile, approximately 92% of people surveyed considered their skills as moderate (26.3%), sufficient (38.1%), or exceptional (27.4%). This finding indicates that electronic device skills among the respondents were at a good level; this is because today, many people use electronic devices for various purposes.
To determine participants’ attitudes toward using EL, they were asked to what extent they have agreed to substitute traditional classes with EL classes. The results are presented in Table 6. The Kruskal–Wallis test was used to determine whether there are differences across the five levels of age group, seven levels of degree of education, and five levels of employment status regarding preference to substitute traditional classes with EL classes. For gender we used the Mann–Whitney U test. The tests revealed significance for all levels of gender, age group, degree of education, and employment status (p = 0.000, p = 0.048, p = 0.044, and p = 0.038, respectively) on the question of replacing traditional classes with EL classes.
According to the results shown in Table 6, most participants with master’s or doctorate degrees (47.1% and 46.5%, respectively) believed that it was somewhat efficient to substitute traditional classes with EL classes. In contrast, most respondents with other degrees ranked the substitution of traditional classes with EL classes as slightly efficient. Compared to those who are older, a large percentage of younger respondents (60.0% of respondents who were between 12–17 years old and 41.6% of respondents who were between 18–24 years old) did not intend to switch from traditional classes to online classes. The majority of students (44.6%) preferred traditional classes over EL classes, whereas the majority of other classes of employment rated swapping traditional classes for EL classes as somewhat efficient. Overall, the results in Table 6 indicate that the majority of respondents in this study believed that it is not suitable to completely replace traditional classes with EL.
This questionnaire had an item regarding the most commonly used important features of EL delivery. The results indicated that the two most common features of EL delivery utilized by the Kurdish people were video conferencing (20.5%) and discussion forums (17.6%), followed by reporting (14.5%), other features of EL delivery (12.5%), chat (9.6%), feedback gathering (9.2%), grading system/assessment (9.1%), training tracking (5.1%), and gamification (game principles) (2.0%). In this question, the participants were allowed to select more than one response. The obvious reason behind using video conferencing by the majority of participants was that, during the COVID-19 pandemic, many schools and universities in the KRI adopted the use of video conferencing tools as educational tools.
For the two questions in Table 6 and Table 7, only the responses of those participants who used EL before or after the pandemic outbreak started were counted. Among the 430 participants, only 312 of them had used EL before or after the pandemic, or during both periods.

5. Discussion

There is consistent scientific evidence that the changes triggered by the COVID-19 pandemic had a significant impact on EL and business technology, and these effects have been uncovered rapidly in the aftermath of the pandemic [44,45]. Articles used different techniques for analyzing data and tackling electronic learning during the pandemic outbreak. For example, Coman et al. [46] highlighted that the coronavirus pandemic has modified two important aspects of the Romanian higher education system: the establishment of a digitalization system; the influence of the transition toward EL. Furthermore, the modifications occurred in a short period. One study on electronic learning was conducted at the University of Timisoara in Romania, with the majority of participants favoring onsite campus learning [30].
As illustrated in Table 1 only 151 (35.1%) of participants used EL before the pandemic. Yet, during the pandemic, there was a significant increase in the use of EL (to 67.9%). This result suggests that the demand for educational technologies, tools, and services has grown. This is consistent with other studies [16,18], who stated that with the appearance of COVID-19, the use and practice of video connections and conferencing have increased significantly, and purchases and sales of goods and services have shifted to online shopping.
The majority of respondents were between the ages of 18 and 44, had a bachelor’s, master’s, or doctorate degree, and were students or teachers. However, while the majority of participants used online learning during COVID-19, there was still a large percentage of non-users (32%). A possible explanation for this result could be some shortcomings of EL providers and the high demand for technological devices. In line with our insight, Maatuk et al. [47] reported that students and teaching staff’s perspectives after the COVID-19 pandemic outbreak for lowest and highest materiality are around (40%, 41%) and (83%, 91%). The result indicated that the population is not prepared for EL due to the effects of EC. Against the background of the pandemic, the set of reasons for which students did not favor online studying were technical issues, which had a substantial impact on students’ engagement. Another important factor is instructors’ lack of technical skills, which mirrors the findings of a previous study conducted during this period. Thus, by solving commercial and technical problems, the EL process can be implemented at a more rapid pace. Based on the degree of support provided, the readiness to embrace technology can be predicted. An example of support would be the degree to which an individual perceives the amount of government assistance, such as decreasing the cost of utilizing the internet. The KRI government’s involvement in equipping citizens with technical skills should be materialized in substantial government resources, awareness campaigns, technical support facilities, and the protection of consumer rights.
This study reports that 32.5% (before COVID-19) and 36.0% (after COVID-19) of our participants propose synchronous online learning as the most reliable form of online education. This evidence supported and was consistent with the results from earlier scientific studies [48,49,50]. Although synchronous online learning was a panacea to overcome the early stages of educational needs during the pandemic, it undoubtedly has negative consequences. According to one study [51], about 30% of learners missed classes, and some students had issues accessing classes, connecting to the internet, and downloading course materials. Moreover, there is a sharp increase from 19.2% to 30.5% in the use of interactive online learning after the pandemic outbreak. This is because, during the pandemic, in most universities teachers not only used EL platforms to conduct online classes but also kept track of their students’ progress by creating assignments, quizzes, and polls.
According to the findings shown in Table 3, LMSs such as Google Classroom and Edmodo have been most widely used before (34.7%) and during the COVID-19 pandemic (35.6%), followed by web conferencing and webinar tools such as Zoom and Google Meet as the second most-used software applications for EL during the pandemic (33.6%). Those findings are in line with a study carried out in Indonesia [52]. On the other hand, the results of a study conducted in Ukraine [53] were slightly different from our study’s findings, since Zoom as an online conferencing tool was the most widespread and Google Classroom as LMS was the second most commonly used.
According to our study, EL use has significantly increased after the pandemic. This has led to an increase in the demand for purchasing tools and electronic devices necessary for the EL process, such as smartphones, laptops, tablets, and desktops. Table 2 illustrates that laptops and smartphones were the two electronic devices that were used by the majority of participants for EL. These technological tools are crucial factors in commercial adoption, and they can also be utilized for conducting online business [54,55]. According to [56], EL in Iraq has grown since the COVID-19 pandemic began. In addition, according to [57], electronic devices were the fifth most frequently purchased products online in Iraq during the pandemic. Therefore, the concept of EL goes beyond simply moving education online as it leads to growth in commercial and marketing activity.
Even though the results obtained in Table 6 show that the majority of participants did not agree when it comes to completely replacing traditional classes with EL permanently, they still believe that EL can be used alongside traditional education. This is because over 32% of respondents stated that they have used EL, as indicated in Table 2. This result was consistent with other studies [58,59], as they believe that face-to-face learning was an effective model and cannot be wholly replaced with online learning. Furthermore, participants [60,61,62,63] also believed that blended learning, which integrates classroom teaching with digital learning, was an effective method for teaching and the learning process.
Regarding the most commonly used important features of EL, about half of the respondents indicated they have utilized video conferencing (18.9%), discussion forums (15.9%), and reporting (15.5%); this is illustrated in Table 7. Video conferencing tools such as Zoom and Google Meet have become familiar and widely used for online learning during the COVID-19 crisis [64,65,66]. These communication tools when used for web conferencing and webinar tools (33.6%), as shown in Table 3 were the second most-used tool during the COVID-19 outbreak by the surveyed participants. According to [67,68], the video conferencing feature can be utilized in online learning to facilitate effective communication and engagement between learners and educators. Another common tool among the surveyed respondents is discussion forums. Using this tool, students have the option to interact with, review, and discuss course materials; this leads students to share, discuss, and process their thoughts and think critically [69,70]. Therefore, despite the teacher’s absence, students feel the presence of the teacher when using discussion forums.

6. Limitations and Suggestions for Further Studies

There is no doubt that some actual limitations exist that have not been assessed for our research. The sample of respondents has been selected from the general population of the KRI and collected data required about three months. As a result, if we want to collect more data than what is currently accessible, we will need to do so over a longer period. The use of this single-item questionnaire might have influenced the outcome. We believe that the non-comparable item we used has poorer specificity than other identified surveys, which may contribute to an overestimation of EL. This study illustrated that we need to find all bridges regarding the gaps between commercial techniques and EL, and provide a guideline on EL initiatives for sustainable education but this is difficult to achieve without using machine learning or deep learning.
As a result, scholars are invited to delve more into areas addressed in this study, such as commerce and operational trends influenced by EL due to coronavirus. Future studies may examine other techniques to find new results on this subject and contribute to general economic growth. Planning should be done incrementally or in large initiatives, with a focus on the risks and possibilities associated with each strategic option. For this important perspective, some complex machine learning features, such as decision trees [71], long short-term memory [72] might be used to evaluate data. Furthermore, our data must be harmonically optimized to find a global solution in utilizing current optimization algorithms or neural network algorithms such as the colony predation algorithm (CPA) [73], learner performance-based behavioral algorithm (LPB) [74], fitness dependent optimizer (FDO) [75], heuristic optimization [76], etc. We make it clear that COVID-19, just as the financial crisis [77], influenced the IT field [78], the liquidity of entities [79], and banks [80] as well as the behavior of taxpayers [81,82].

7. Conclusions

COVID-19 has emerged as a global health threat. Researchers are attempting to identify the characteristics that have been impacted by this pandemic. Thus, the goal of this study was to assess the degree of the use of EL during quarantine due to COVID-19 and the effectiveness of EL on the sustainable growth of the EC sector. The outcomes suggested that implementing EL with the spread of the coronavirus had an impact on the entire EC sector and teaching methods, and as such the EL independent variables were the most important in determining the returns of company stocks and new methods of study. The devices were determined based on the most frequently used types. These selections might have guided future decisions. For example, students used smartphones for general education purposes (47.2%), while instructors used laptops for general education (68.5%), as well as conferences, seminars, workshops, and symposiums. In the KRI, the use of technology to access the internet expanded after the COVID-19 outbreak, increasing from 35% to 68%. In conclusion, this study suggests that the government should identify the best alternatives for addressing the difficulties that have affected consumers, particularly with regard to commercial trading and new methodologies of teaching.
Finally, these findings provide practical suggestions for striking a balance between economic enterprise and EL. To have a sustainable education, key stakeholders may consider using blended EL, which combines face-to-face and online learning. Free EL learning games and simple software applications could be used to create balance, as suggested by Nagurney [83].

Author Contributions

Data curation, writing—original draft, writing—review and editing, formal analysis, D.O.H.; Data curation, writing—original draft, writing—review and editing, methodology, A.M.A.; Data curation, visualization, validation, A.A.H.A.; Writing—review and editing, supervision, T.A.R.; Investigation, Y.H.A.; Investigation, supervision, M.A.-B.; Investigation, visualization, J.M.; Funding acquisition, writing—review and editing, supervision, I.B.; Investigation, writing—review and editing, visualization, E.S.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Yuan, Y.-P.; Tan, G.W.-H.; Ooi, K.-B.; Lim, W.-L. Can COVID-19 Pandemic Influence Experience Response in Mobile Learning? Telemat. Inform. 2021, 64, 101676. [Google Scholar] [CrossRef]
  2. Alsoud, A.R.; Harasis, A.A. The Impact of COVID-19 Pandemic on Student’s e-Learning Experience in Jordan. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 1404–1414. [Google Scholar] [CrossRef]
  3. Sumi, R.S.; Kabir, G. Satisfaction of E-Learners with Electronic Learning Service Quality Using the SERVQUAL Model. J. Open Innov. Technol. Mark. Complex. 2021, 7, 227. [Google Scholar] [CrossRef]
  4. Radu, M.-C.; Schnakovszky, C.; Herghelegiu, E.; Ciubotariu, V.-A.; Cristea, I. The Impact of the COVID-19 Pandemic on the Quality of Educational Process: A Student Survey. Int. J. Environ. Res. Public Health 2020, 17, 7770. [Google Scholar] [CrossRef] [PubMed]
  5. Pokhrel, S.; Chhetri, R. A Literature Review on Impact of COVID-19 Pandemic on Teaching and Learning. High. Educ. Future 2021, 8, 133–141. [Google Scholar] [CrossRef]
  6. Nanda, A.; Xu, Y.; Zhang, F. How Would the COVID-19 Pandemic Reshape Retail Real Estate and High Streets through Acceleration of E-Commerce and Digitalization? J. Urban Manag. 2021, 10, 110–124. [Google Scholar] [CrossRef]
  7. Aguilera-Hermida, A.P. College Students’ Use and Acceptance of Emergency Online Learning Due to COVID-19. Int. J. Educ. Res. Open 2020, 1, 100011. [Google Scholar] [CrossRef]
  8. Vargo, D.; Zhu, L.; Benwell, B.; Yan, Z. Digital Technology Use during COVID-19 Pandemic: A Rapid Review. Hum. Behav. Emerg. Technol. 2021, 3, 13–24. [Google Scholar] [CrossRef]
  9. Cohen-Mansfield, J.; Muff, A.; Meschiany, G.; Lev-Ari, S. Adequacy of Web-Based Activities as a Substitute for in-Person Activities for Older Persons during the COVID-19 Pandemic: Survey Study. J. Med. Internet Res. 2021, 23, e25848. [Google Scholar] [CrossRef]
  10. Ahmed, S.Y.; Ali, B.J.; Top, C. Understanding the Impact of Trust, Perceived Risk, and Perceived Technology on the Online Shopping Intentions: Case Study in Kurdistan Region of Iraq. J. Contemp. Issues Bus. Gov. 2021, 27, 2136–2153. [Google Scholar]
  11. Petronzi, R.; Petronzi, D. The Online and Campus (OaC) Model as a Sustainable Blended Approach to Teaching and Learning in Higher Education: A Response to COVID-19. J. Pedagog. Res. 2020, 4, 498–507. [Google Scholar] [CrossRef]
  12. Holotescu, C.; Grosseck, G.; Andone, D.; Gunesch, L.; Constandache, L.; Nedelcu, V.D.; Ivanova, M.S.; Dumbrăveanu, R. Romanian Educational System Response during the COVID-19 Pandemic. In Proceedings of the eLearning and Software for Education Conference, Bucharest, Romania, 23–24 April 2020; pp. 11–19. [Google Scholar]
  13. Martínez-Córdoba, P.-J.; Benito, B.; García-Sánchez, I.-M. Efficiency in the Governance of the Covid-19 Pandemic: Political and Territorial Factors. Glob. Health 2021, 17, 113. [Google Scholar] [CrossRef] [PubMed]
  14. Ivanec, T.P. The Lack of Academic Social Interactions and Students’ Learning Difficulties during COVID-19 Faculty Lockdowns in Croatia: The Mediating Role of the Perceived Sense of Life Disruption Caused by the Pandemic and the Adjustment to Online Studying. Soc. Sci. 2022, 11, 42. [Google Scholar] [CrossRef]
  15. Pan, S.L.; Zhang, S. From Fighting COVID-19 Pandemic to Tackling Sustainable Development Goals: An Opportunity for Responsible Information Systems Research. Int. J. Inf. Manag. 2020, 55, 102196. [Google Scholar] [CrossRef] [PubMed]
  16. Mouratidis, K.; Papagiannakis, A. COVID-19, Internet, and Mobility: The Rise of Telework, Telehealth, e-Learning, and e-Shopping. Sustain. Cities Soc. 2021, 74, 103182. [Google Scholar] [CrossRef]
  17. Xhelili, P.; Ibrahimi, E.; Rruci, E.; Sheme, K. Adaptation and Perception of Online Learning during COVID-19 Pandemic by Albanian University Students. Int. J. Stud. Educ. 2021, 3, 103–111. [Google Scholar] [CrossRef]
  18. Mouratidis, K.; Peters, S. COVID-19 Impact on Teleactivities: Role of Built Environment and Implications for Mobility. Transp. Res. Part A Policy Pract. 2022, 158, 251–270. [Google Scholar] [CrossRef]
  19. Alabduljabbar, A.; Aksoy, M. The Effect of COVID-19 on E-Commerce and E-Learning. Int. J. Sci. Res. (IJSR) 2021, 10, 751–755. [Google Scholar]
  20. Gu, S.; Ślusarczyk, B.; Hajizada, S.; Kovalyova, I.; Sakhbieva, A. Impact of the Covid-19 Pandemic on Online Consumer Purchasing Behavior. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 2263–2281. [Google Scholar] [CrossRef]
  21. Susanto, H.; Fang Yie, L.; Mohiddin, F.; Rahman Setiawan, A.A.; Haghi, P.K.; Setiana, D. Revealing Social Media Phenomenon in Time of COVID-19 Pandemic for Boosting Start-up Businesses through Digital Ecosystem. Appl. Syst. Innov. 2021, 4, 6. [Google Scholar] [CrossRef]
  22. Guthrie, C.; Fosso-Wamba, S.; Arnaud, J.B. Online Consumer Resilience during a Pandemic: An Exploratory Study of e-Commerce Behavior before, during and after a COVID-19 Lockdown. J. Retail. Consum. Serv. 2021, 61, 102570. [Google Scholar] [CrossRef]
  23. Hashem, T.N. Examining the Influence of Covid 19 Pandemic in Changing Customers’ Orientation towards e-Shopping. Mod. Appl. Sci. 2020, 14, 59–76. [Google Scholar] [CrossRef]
  24. Carlsson-Szlezak, P.; Reeves, M.; Swartz, P. What Coronavirus Could Mean for the Global Economy. Harv. Bus. Rev. 2020, 3, 1–10. [Google Scholar]
  25. Rose-Redwood, R.; Kitchin, R.; Apostolopoulou, E.; Rickards, L.; Blackman, T.; Crampton, J.; Rossi, U.; Buckley, M. Geographies of the COVID-19 Pandemic. Dialogues Hum. Geogr. 2020, 10, 97–106. [Google Scholar] [CrossRef]
  26. Akour, I.; Alshurideh, M.; al Kurdi, B.; al Ali, A.; Salloum, S. Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms during the COVID-19 Pandemic: Machine Learning Approach. JMIR Med. Educ. 2021, 7, e24032. [Google Scholar] [CrossRef]
  27. Dwivedi, Y.K.; Hughes, D.L.; Coombs, C.; Constantiou, I.; Duan, Y.; Edwards, J.S.; Gupta, B.; Lal, B.; Misra, S.; Prashant, P. Impact of COVID-19 Pandemic on Information Management Research and Practice: Transforming Education, Work and Life. Int. J. Inf. Manag. 2020, 55, 102211. [Google Scholar] [CrossRef]
  28. Elumalai, K.V.; Sankar, J.P.; Kalaichelvi, R.; John, J.A.; Menon, N.; Alqahtani, M.S.M.; Abumelha, M.A. Factors Affecting the Quality of E-Learning during the COVID-19 Pandemic from the Perspective of Higher Education Students. COVID-19 Educ. Learn. Teach. A Pandemic-Constrained Environ. 2020, 19, 731–753. [Google Scholar]
  29. Saxena, C.; Baber, H.; Kumar, P. Examining the Moderating Effect of Perceived Benefits of Maintaining Social Distance on E-Learning Quality during COVID-19 Pandemic. J. Educ. Technol. Syst. 2021, 49, 532–554. [Google Scholar] [CrossRef]
  30. Gherheș, V.; Stoian, C.E.; Fărcașiu, M.A.; Stanici, M. E-Learning vs. Face-to-Face Learning: Analyzing Students’ Preferences and Behaviors. Sustainability 2021, 13, 4381. [Google Scholar] [CrossRef]
  31. Alexa, L.; Avasilcai, S.; Pislaru, M.; Bujor, A.; Avram, E.; Lucescu, L. Exploring Romanian Engineering Students’ Perceptions of Covid-19 Emergency e-Learning Situation. A Mixed-Method Case Study. Electron. J. E-Learn. 2022, 20, 19–35. [Google Scholar] [CrossRef]
  32. Zamora-Antuñano, M.A.; Rodríguez-Reséndiz, J.; Rodriguez Segura, L.; Cruz Perez, M.A.; Altamirano Corro, J.A.; Paredes-Garcia, W.J.; Rodríguez-Reséndiz, H. Analysis of Emergency Remote Education in COVID-19 Crisis Focused on the Perception of the Teachers. Sustainability 2021, 13, 3820. [Google Scholar] [CrossRef]
  33. Liu, Z. Learning from E-Commerce for E-Learning. In Proceedings of the 2007 First IEEE International Symposium on Information Technologies and Applications in Education, Kunming, China, 23–25 November 2007; Curran Associates: New York, NY, USA, 2007; pp. 193–197. [Google Scholar]
  34. Balida, A.R.; Encarnacion, R. Challenges and Relationships of E-Learning Tools to Teaching and Learning. In Proceedings of the European Conference on E-Learning, Berlin, Germany, 28–30 October 2020; Academic Conferences International: Berlin, Germany, 2020; p. 48-XIX. [Google Scholar]
  35. Spennemann, D.H.R. Persistence and Attrition among Participants in a Multi-Page Online Survey Recruited via Reddit’s Social Media Network. Soc. Sci. 2022, 11, 31. [Google Scholar] [CrossRef]
  36. Şahintürk, L.; Özcan, B. The Comparison of Hypothesis Tests Determining Normality and Similarity of Samples. J. Nav. Sci. Eng. 2017, 13, 21–36. [Google Scholar]
  37. Soong, T.R.; Milner, D.A., Jr. Comparative Statistics. In Statistics for Pathologists; Springer: Berlin/Heidelberg, Germany, 2016; Volume 39. [Google Scholar]
  38. Ostertagova, E.; Ostertag, O.; Kováč, J. Methodology and Application of the Kruskal-Wallis Test. In Applied Mechanics and Materials; Trans Tech Publications: Wollerau, Switzerland, 2014; Volume 611, pp. 115–120. [Google Scholar]
  39. Costa, P.S.; Santos, N.C.; Cunha, P.; Cotter, J.; Sousa, N. The Use of Multiple Correspondence Analysis to Explore Associations between Categories of Qualitative Variables in Healthy Ageing. J. Aging Res. 2013, 2013, 302163. [Google Scholar] [CrossRef] [Green Version]
  40. White, D.; Korotayev, A. Statistical Analysis of Cross-Tabs. Anthrosciences.org, 2004. [Google Scholar]
  41. Dura, C.; Drigă, I. Application of Chi Square Test in Marketing Research. Ann. Univ. Petroşani. Econ. 2017, 17, 79–90. [Google Scholar]
  42. Dwivedi, Y.K.; Ismagilova, E.; Hughes, D.L.; Carlson, J.; Filieri, R.; Jacobson, J.; Jain, V.; Karjaluoto, H.; Kefi, H.; Krishen, A.S. Setting the Future of Digital and Social Media Marketing Research: Perspectives and Research Propositions. Int. J. Inf. Manag. 2021, 59, 102168. [Google Scholar] [CrossRef]
  43. Al-Maroof, R.S.; Alhumaid, K.; Akour, I.; Salloum, S. Factors That Affect E-Learning Platforms after the Spread of COVID-19: Post Acceptance Study. Data 2021, 6, 49. [Google Scholar] [CrossRef]
  44. Mofijur, M.; Fattah, I.M.R.; Alam, M.A.; Islam, A.B.M.S.; Ong, H.C.; Rahman, S.M.A.; Najafi, G.; Ahmed, S.F.; Uddin, M.A.; Mahlia, T.M.I. Impact of COVID-19 on the Social, Economic, Environmental and Energy Domains: Lessons Learnt from a Global Pandemic. Sustain. Prod. Consum. 2021, 26, 343–359. [Google Scholar] [CrossRef] [PubMed]
  45. Xu, Y.; Li, J.-P.; Chu, C.-C.; Dinca, G. Impact of COVID-19 on Transportation and Logistics: A Case of China. Econ. Res.-Ekon. Istraživanja 2021, 35, 2386–2404. [Google Scholar] [CrossRef]
  46. Coman, C.; Țîru, L.G.; Meseșan-Schmitz, L.; Stanciu, C.; Bularca, M.C. Online Teaching and Learning in Higher Education during the Coronavirus Pandemic: Students’ Perspective. Sustainability 2020, 12, 10367. [Google Scholar] [CrossRef]
  47. Maatuk, A.M.; Elberkawi, E.K.; Aljawarneh, S.; Rashaideh, H.; Alharbi, H. The COVID-19 Pandemic and E-Learning: Challenges and Opportunities from the Perspective of Students and Instructors. J. Comput. High Educ. 2022, 34, 21–38. [Google Scholar] [CrossRef] [PubMed]
  48. Zhang, K.; Wu, H. Synchronous Online Learning During COVID-19: Chinese University EFL Students’ Perspectives. Sage Open 2022, 12, 21582440221094820. [Google Scholar] [CrossRef]
  49. Schlenz, M.A.; Schmidt, A.; Wöstmann, B.; Krämer, N.; Schulz-Weidner, N. Students’ and Lecturers’ Perspective on the Implementation of Online Learning in Dental Education Due to SARS-CoV-2 (COVID-19): A Cross-Sectional Study. BMC Med. Educ. 2020, 20, 354. [Google Scholar] [CrossRef] [PubMed]
  50. Camilleri, M.A.; Camilleri, A.C. Remote Learning via Video Conferencing Technologies: Implications for Research and Practice. Technol. Soc. 2022, 68, 101881. [Google Scholar] [CrossRef]
  51. Mahyoob, M. Challenges of E-Learning during the COVID-19 Pandemic Experienced by EFL Learners. Arab. World Engl. J. (AWEJ) 2020, 11, 351–362. [Google Scholar] [CrossRef]
  52. Irfan, M.; Kusumaningrum, B.; Yulia, Y.; Widodo, S.A. Challenges during the Pandemic: Use of e-Learning in Mathematics Learning in Higher Education. Infin. J. 2020, 9, 147–158. [Google Scholar] [CrossRef]
  53. Grynyuk, S.; Kovtun, O.; Sultanova, L.; Zheludenko, M.; Zasluzhena, A.; Zaytseva, I. Distance Learning During the COVID-19 Pandemic: The Experience of Ukraine’s Higher Education System. Electron. J. e-Learn. 2022, 20, 242–256. [Google Scholar] [CrossRef]
  54. Amornkitvikai, Y.; Tham, S.Y.; Harvie, C.; Buachoom, W.W. Barriers and Factors Affecting the E-Commerce Sustainability of Thai Micro-, Small-and Medium-Sized Enterprises (MSMEs). Sustainability 2022, 14, 8476. [Google Scholar] [CrossRef]
  55. Walker, J.H.; Saffu, K.; Mazurek, M. An Empirical Study of Factors Influencing E-Commerce Adoption/Non-Adoption in Slovakian SMEs. J. Internet Commer. 2016, 15, 189–213. [Google Scholar] [CrossRef]
  56. Ali, B.J. Impact of COVID-19 on Consumer Buying Behavior toward Online Shopping in Iraq. Econ. Stud. J. 2020, 18, 267–280. [Google Scholar]
  57. Al-Sibahee, E.; Halim, R.; Ramadani, H.; Jamal, M. Online Shopping: A Glimpse into the Iraqi Customer Shopping Behavior. Available online: https://bit.ly/3KXsV3g (accessed on 20 July 2022).
  58. Gupta, V.; Jain, N. Harnessing Information and Communication Technologies for Effective Knowledge Creation: Shaping the Future of Education. J. Enterp. Inf. Manag. 2017, 30, 831–855. [Google Scholar] [CrossRef]
  59. Costado Dios, M.T.; Piñero Charlo, J.C. Face-to-Face vs. e-Learning Models in the COVID-19 Era: Survey Research in a Spanish University. Educ. Sci. 2021, 11, 293. [Google Scholar] [CrossRef]
  60. Colreavy-Donelly, S.; Ryan, A.; O’Connor, S.; Caraffini, F.; Kuhn, S.; Hasshu, S. A Proposed VR Platform for Supporting Blended Learning Post COVID-19. Educ. Sci. 2022, 12, 435. [Google Scholar] [CrossRef]
  61. Han, Y.; Wu, W.; Zhang, L.; Liang, Y. Online Blended Learning in Small Private Online Course. Appl. Sci. 2021, 11, 7100. [Google Scholar] [CrossRef]
  62. Al-Fodeh, R.S.; Alwahadni, A.M.S.; Abu Alhaija, E.S.; Bani-Hani, T.; Ali, K.; Daher, S.O.; Daher, H.O. Quality, Effectiveness and Outcome of Blended Learning in Dental Education during the Covid Pandemic: Prospects of a Post-Pandemic Implementation. Educ. Sci. 2021, 11, 810. [Google Scholar] [CrossRef]
  63. Sitthiworachart, J.; Joy, M.; Mason, J. Blended Learning Activities in an E-Business Course. Educ. Sci. 2021, 11, 763. [Google Scholar] [CrossRef]
  64. Dutta, A. Impact of Digital Social Media on Indian Higher Education: Alternative Approaches of Online Learning during COVID-19 Pandemic Crisis. Int. J. Sci. Res. Publ. 2020, 10, 604–611. [Google Scholar] [CrossRef]
  65. Al-Amin, M.; al Zubayer, A.; Deb, B.; Hasan, M. Status of Tertiary Level Online Class in Bangladesh: Students’ Response on Preparedness, Participation and Classroom Activities. Heliyon 2021, 7, e05943. [Google Scholar] [CrossRef]
  66. Falk, M.T.; Hagsten, E. When International Academic Conferences Go Virtual. Scientometrics 2021, 126, 707–724. [Google Scholar] [CrossRef]
  67. Tao, D.; Li, W.; Qin, M.; Cheng, M. Understanding Students’ Acceptance and Usage Behaviors of Online Learning in Mandatory Contexts: A Three-Wave Longitudinal Study during the COVID-19 Pandemic. Sustainability 2022, 14, 7830. [Google Scholar] [CrossRef]
  68. Fatani, T.H. Student Satisfaction with Videoconferencing Teaching Quality during the COVID-19 Pandemic. BMC Med. Educ. 2020, 20, 396. [Google Scholar] [CrossRef]
  69. Cendra, A.N.; Sulindra, E. Navigating Teaching during Pandemic: The Use of Discussion Forum in Business English Writing Class. ELTR J. 2022, 6. [Google Scholar] [CrossRef]
  70. Kilinc, H.; Altinpulluk, H. Discussion Forums as a Learning Material in Higher Education Institutions. Int. J. High. Educ. Pedagog. 2021, 2, 1–9. [Google Scholar] [CrossRef]
  71. Song, Y.-Y.; Ying, L.U. Decision Tree Methods: Applications for Classification and Prediction. Shanghai Arch. Psychiatry 2015, 27, 130. [Google Scholar]
  72. Graves, A. Long Short-Term Memory. In Supervised Sequence Labelling with Recurrent Neural Networks; Springer: Berlin, Germany, 2012; pp. 37–45. [Google Scholar]
  73. Tu, J.; Chen, H.; Wang, M.; Gandomi, A.H. The Colony Predation Algorithm. J. Bionic Eng. 2021, 18, 674–710. [Google Scholar] [CrossRef]
  74. Rahman, C.M.; Rashid, T.A. A New Evolutionary Algorithm: Learner Performance Based Behavior Algorithm. Egypt. Inform. J. 2021, 22, 213–223. [Google Scholar] [CrossRef]
  75. Abdullah, J.M.; Ahmed, T. Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process. IEEE Access 2019, 7, 43473–43486. [Google Scholar] [CrossRef]
  76. Hasan, D.O.; Aladdin, A.M.; Talabani, H.S.; Rashid, T.A.; Mirjalili, S. The Fifteen Puzzle—A New Approach through Hybridizing Three Heuristics Methods. Computers 2023, 12, 11. [Google Scholar] [CrossRef]
  77. Batrancea, L.-M.; Batrancea, I.; Moscviciov, A. The Roots of the World Financial Crisis. Ann. Univ. Oradea Econ. Sci. 2009, 3, 57–62. [Google Scholar]
  78. Moscviciov, A.; Batrancea, I.; Batrancea, M.; Batrancea, L. Financial Ratio Analysis Used in the IT Enterprises. Ann. Univ. Oradea Econ. Sci. 2010, 1, 600–603. [Google Scholar]
  79. Batrancea, L.; Batrancea, I.; Moscviciov, A. The Analysis of the Entity’s Liquidity—A Means of Evaluating Cash Flow. J. Int. Financ. Econ. 2009, 9, 92–98. [Google Scholar]
  80. Batrancea, L.M. An Econometric Approach on Performance, Assets, and Liabilities in a Sample of Banks from Europe, Israel, United States of America, and Canada. Mathematics 2021, 9, 3178. [Google Scholar] [CrossRef]
  81. Nichita, R.-A.; Bătrâncea, L.-M. The Implications of Tax Morale on Tax Compliance Behavior. Ann. Univ. Oradea Econ. Sci. 2012, 21, 739–744. [Google Scholar]
  82. Batrancea, L.; Nichita, R.; Batrancea, I. Tax Non-Compliance Behavior in the Light of Tax Law Complexity and the Relationship between Authorities and Taxpayers. Sci. Ann. “Al. I. Cuza” Univ.-Econ. 2012, 59, 97–106. [Google Scholar] [CrossRef] [Green Version]
  83. Nagurney, A. I Have to Go Online Next Week?! Practical Suggestions Based on Ke (2016). Educ. Technol. Res. Dev. 2021, 69, 149–151. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Comparisons of features (gender, age group, employment status, and degree of education) between respondents who used and or not EL before and after COVID-19.
Figure 1. Comparisons of features (gender, age group, employment status, and degree of education) between respondents who used and or not EL before and after COVID-19.
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Figure 2. Using EL before and after the COVID-19 outbreak.
Figure 2. Using EL before and after the COVID-19 outbreak.
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Table
Table 1. Comparison of demographic characteristics between participants who used and did not use EL before and after COVID-19 with chi-square tests.
Table 1. Comparison of demographic characteristics between participants who used and did not use EL before and after COVID-19 with chi-square tests.
FeaturesTotal
Sample		EL before COVID-19EL after COVID-19
UsingNot UsingStatisticsUsingNot UsingStatistics
n = 430151 (35.1%)279 (64.9%)292 (67.9%)138 (32.1%)
Gender
Male235 (54.7%)88 (37.4%)147 (62.6%)χ2 (1) = 1.02,
p = 0.313		163 (69.4%)72 (30.6%)χ2 (1) = 0.367,
p = 0.545
Female195 (45.3%)63 (32.3%)132 (67.7%)129 (66.2%)66 (33.8%)
Age Group
12–17 years old10 (2.3%)2 (20.0%)8 (80.0%)χ2 (4) = 7.7,
p = 0.103		4 (40.0%)6 (60.0%)χ2 (4) = 14.985,
p = 0.005
18–24 years old178 (41.4%)53 (29.8%)125 (70.2%)109 (61.2%)69 (38.8%)
25–34 years old91 (21.2%)32 (35.2%)59 (64.8%)65 (71.4%)26 (28.6%)
35–44 years old117 (27.2%)52 (44.4%)65 (55.6%)84 (71.8%)33 (28.2%)
45–54 years old34 (7.9%)12 (35.3%)22 (64.7%)30 (88.2%)4 (11.8%)
Degree of Education
No schooling completed29 (6.7%)7 (24.1%)22 (75.9%)χ2 (6) = 21.72,
p = 0.00136		14 (48.3%)15 (51.7%)χ2 (6) = 38.29,
p = 0.000001
Primary (basic education)7 (1.6%)0 (0.0%)7 (100.0%)4 (57.1%)3 (42.9%)
High school, no diploma31 (7.2%)9 (29.0%)22 (71.0%)16 (51.6%)15 (48.4%)
High school, diploma or the equivalent26 (6.0%)9 (34.6%)17 (65.4%)14 (53.8%)12 (46.2%)
Bachelor degree190 (44.2%)55 (28.9%)135 (71.1%)117 (61.6%)73 (38.4%)
Master degree104 (24.2%)47 (45.2%)57 (54.8%)89 (85.6%)15 (14.4%)
Doctorate degree43 (10.0%)24 (55.8%)19 (44.2%)38 (88.4%)5 (11.6%)
Employment status
Employed74 (17.2%)23 (31.1%)51 (68.9%)χ2 (4) = 14.51,
p = 0.006		50 (67.6%)24 (32.4%)χ2 (4) = 12.417,
p = 0.015
Unemployed
(no work)		12 (2.8%)5 (41.7%)7 (58.3%)7 (58.3%)5 (41.7%)
Student177 (41.2%)47 (26.6%)130 (73.4%)108 (61.0%)69 (39.0%)
Teacher141 (32.8%)65 (46.1%)76 (53.9%)111 (78.7%)30 (21.3%)
Other26 (6.0%)11 (42.3%)15 (57.7%)16 (61.5%)10 (38.5%)
Abbreviations: n—numbers of respondents, χ2 = chi-square, p—statistical significance. Bold values are used to emphasize statistical significance (p < 0.05).
Table
Table 2. Effects of the COVID-19 pandemic on EL.
Table 2. Effects of the COVID-19 pandemic on EL.
Classification FeaturesBefore COVID-19Classification FeaturesAfter COVID-19
n = 151n = 292
The most popular EL method usedSynchronous online learning49 (32.5%)Synchronous online learning105 (36.0%)
Asynchronous online learning27 (17.9%)Asynchronous online learning50 (17.1%)
Linear e-learning27 (17.9%)Linear e-learning33 (11.3%)
Interactive online learning29 (19.2%)Interactive online learning89 (30.5%)
Others19 (12.6%)Others15 (5.1%)
The most preferred device used for the EL methodSmartphone57 (37.7%)Smartphone115 (39.4%)
Tablet2 (1.3%)Tablet7 (2.4%)
Laptop81 (53.6%)Laptop155 (53.1%)
Desktop11 (7.3%)Desktop14 (4.8%)
Others0 (0)Others1 (0.3%)
The scope or level of the EL method usedGeneral education74 (49.0%)General education124 (42.5%)
Training session23 (15.2%)Training session40 (13.7%)
Companies or organizations6 (4.0%)Companies or organizations11 (3.8%)
Conferences, seminars, workshops, or symposiums30 (19.9%)Conferences, seminars, workshops, or symposiums73 (25.0%)
Others18 (11.9%)Others44 (15.1%)
The period of using EL as an instructional platformApproximately one year71 (47.0%)Approximately six months82 (28.1%)
Approximately two years35 (23.2%)Approximately one year60 (20.5%)
Approximately three years11 (7.3%)Approximately two years55 (18.8%)
Approximately four years10 (6.6%)Until now95 (32.5%)
More than four years24 (15.9%)
Table
Table 3. Participants’ answers to the question: What type of software applications have you used the most for the EL method before the COVID-19 outbreak started.
Table 3. Participants’ answers to the question: What type of software applications have you used the most for the EL method before the COVID-19 outbreak started.
Classification FeaturesBefore COVID-19After COVID-19
Learning management systems (LMSs) (Moodle; Google Classroom; Edmodo; Adobe Captivate Prime)69 (34.7%)143 (35.6%)
Mobile tools as an e-learning platform (Viber; WhatsApp; Telegram; Messenger; WeChat)61 (30.7%)87 (21.6%)
Virtual classroom software (Newrow Smart; Blackboard Collaborate)17 (8.5%)37 (9.2%)
Web conferencing and webinar tools (Zoom Meetings; Google Meet)52 (26.1%)135 (33.6%)
Table
Table 4. Participants’ answers to various questions.
Table 4. Participants’ answers to various questions.
FeaturesClassification FeaturesAfter COVID-19Statistical Models
StudentTeacherEmployedUnemployedOther
The most popular EL method usedSynchronous online learning41 (38.0%)36 (32.4%)21 (42.0%)3 (42.9%)4 (25.0%)χ2 (16) = 26.159, p = 0.052
Asynchronous online learning13 (12.0%)26 (23.4%)3 (6.0%)4 (57.1%)4 (25.0%)
Linear e-learning14 (13.0%)13 (11.7%)4 (8.0%)0 (0.0%)2 (12.5%)
Interactive online learning37 (34.3%)29 (26.1%)19 (38.0%)0 (0.0%)4 (25.0%)
Others3 (2.8%)7 (6.3%)3 (6.0%)0 (0.0%)2 (12.5%)
The most preferred device used for the EL methodSmartphone51 (47.2%)28 (25.2%)22 (44.0%)4 (57.1%)10 (62.5%)χ2 (16) = 26.833, p = 0.043
Tablet2 (1.9%)4 (3.6%)0 (0.0%)0 (0.0%)1 (6.2%)
Laptop47 (43.5%)76 (68.5%)25 (50.0%)3 (42.9%)4 (25.0%)
Desktop7 (6.5%)3 (2.7%)3 (6.0%)0 (0.0%)1 (6.2%)
Others1 (0.9%)0 (0.0%)0 (0.0%)0 (0.0%)0 (0.0%)
The scope or level of the EL method utilizedGeneral education51 (47.2%)48 (43.2%)17 (34.0%)2 (28.6%)6 (37.5%)χ2 (16) =58.37, p = 0.000001
Training session13 (12.0%)11 (9.9%)8 (16.0%)3 (42.9%)5 (31.2%)
Companies or organizations6 (5.6%)0 (0.0%)3 (6.0%)0 (0.0%)2 (12.5%)
Conferences, seminars, workshops, or symposiums14 (13.0%)47 (42.3%)11 (22.0%)1 (14.3%)0 (0.0%)
Others24 (22.2%)5 (4.5%)11 (22.0%)1 (14.3%)3 (18.8%)
The period of using EL as an instructional platformApproximately six months37 (34.3%)19 (17.1%)17 (34.0%)3 (42.9%)6 (37.5%)χ2 (12) = 24.509, p = 0.017
Approximately one year28 (25.9%)23 (20.7%)5 (10.0%)0 (0.0%)4 (25.0%)
Approximately two years11 (10.2%)30 (27.0%)11 (22.0%)2 (28.6%)1 (6.2%)
Until now32 (29.6%)39 (35.1%)17 (34.0%)2 (28.6%)5 (31.2%)
Table
Table 5. Participants’ answers to the question: Skill in using electronic devices.
Table 5. Participants’ answers to the question: Skill in using electronic devices.
Classification FeaturesTotal SamplePercentage
n = 430(%)
Poor11(2.6%)
Insufficient24(5.6%)
Moderate113(26.3%)
Sufficient164(38.1%)
Exceptional118(27.4%)
Table
Table 6. Participants’ answers to the question: To what extent do you agree to substitute traditional classes with EL classes?
Table 6. Participants’ answers to the question: To what extent do you agree to substitute traditional classes with EL classes?
To What Extent Do You Agree to Substitute Traditional Classes with EL Classes?
Very Slightly EfficientSlightly EfficientSomewhat EfficientVery EfficientExtremely EfficientStatistical Models
141 (32.8%)97 (22.6%)145 (33.7%)39 (9.1%)8 (1.9%)
Gender
Male74 (31.5%)50 (21.3%)85 (36.2%)22 (9.4%)4 (1.7%)p = 0.000 a
Female67 (34.4%)47 (24.1%)60 (30.8%)17 (8.7%)4 (2.1%)
Age Group
12–17 years old6 (60.0%)3 (30.0%)1 (10.0%)0 (0.0%)0 (0.0%)p = 0.048 b
18–24 years old74 (41.6%)32 (18.0%)45 (25.3%)21 (11.8%)6 (3.4%)
25–34 years old24 (26.4%)26 (28.6%)31 (34.1%)9 (9.9%)1 (1.1%)
35–44 years old28 (23.9%)28 (23.9%)51 (43.6%)9 (7.7%)1 (0.9%)
45–54 years old9 (26.5%)8 (23.5%)17 (50.0%)0 (0.0%)0 (0.0%)
Degree of education
No schooling completed12 (41.4%)9 (31.0%)5 (17.2%)3 (10.3%)0 (0.0%)p = 0.044 b
Primary (basic education)4 (57.1%)1 (14.3%)2 (28.6%)0 (0.0%)0 (0.0%)
High school, no diploma15 (48.4%)5 (16.1%)6 (19.4%)2 (6.5%)3 (9.7%)
High school, diploma or the equivalent11 (42.3%)4 (15.4%)7 (26.9%)4 (15.4%)0 (0.0%)
Bachelor’s degree69 (36.3%)42 (22.1%)56 (29.5%)19 (10.0%)4 (2.1%)
Master’s degree19 (18.3%)26 (25.0%)49 (47.1%)9 (8.7%)1 (1.0%)
Doctorate degree11 (25.6%)10 (23.3%)20 (46.5%)2 (4.7%)0 (0.0%)
Employment status
Unemployed3 (25.0%)2 (16.7%)4 (33.3%)3 (25.0%)0 (0.0%)p = 0.038 b
Employed15 (20.3%)17 (23.0%)36 (48.6%)5 (6.8%)1 (1.4%)
Student79 (44.6%)33 (18.6%)38 (21.5%)21 (11.9%)6 (3.4%)
Teacher36 (25.5%)39 (27.7%)58 (41.1%)7 (5.0%)1 (0.7%)
Other8 (30.8%)6 (23.1%)9 (34.6%)3 (11.5%)0 (0.0%)
a Mann–Whitney U test. b Kruskal–Wallis test. Bold values are used to emphasize statistical significance (p < 0.05).
Table
Table 7. Participants answer the question: The most commonly used important features of EL delivery.
Table 7. Participants answer the question: The most commonly used important features of EL delivery.
Classification FeaturesTotal SamplePercentage
Chat84(12.1%)
Discussion forums111(15.9%)
Feedback gathering55(7.9%)
Gamification (game principles)18(2.6%)
Grading system/assessment56(8.0%)
Others99(14.2%)
Training tracking34(4.9%)
Video conferencing132(18.9%)
reporting108(15.5%)
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Hasan, D.O.; Aladdin, A.M.; Amin, A.A.H.; Rashid, T.A.; Ali, Y.H.; Al-Bahri, M.; Majidpour, J.; Batrancea, I.; Masca, E.S. Perspectives on the Impact of E-Learning Pre- and Post-COVID-19 Pandemic—The Case of the Kurdistan Region of Iraq. Sustainability 2023, 15, 4400. https://doi.org/10.3390/su15054400

AMA Style

Hasan DO, Aladdin AM, Amin AAH, Rashid TA, Ali YH, Al-Bahri M, Majidpour J, Batrancea I, Masca ES. Perspectives on the Impact of E-Learning Pre- and Post-COVID-19 Pandemic—The Case of the Kurdistan Region of Iraq. Sustainability. 2023; 15(5):4400. https://doi.org/10.3390/su15054400

Chicago/Turabian Style

Hasan, Dler O., Aso M. Aladdin, Azad Arif Hama Amin, Tarik A. Rashid, Yossra H. Ali, Mahmood Al-Bahri, Jafar Majidpour, Ioan Batrancea, and Ema Speranta Masca. 2023. "Perspectives on the Impact of E-Learning Pre- and Post-COVID-19 Pandemic—The Case of the Kurdistan Region of Iraq" Sustainability 15, no. 5: 4400. https://doi.org/10.3390/su15054400

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