research paper on e learning during covid 19

E-Learning in Time of Covid-19 Pandemic: Challenges & Experiences

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ORIGINAL RESEARCH article

The impact of e-learning systems on motivating students and enhancing their outcomes during covid-19: a mixed-method approach.

• 1 University of Oum El Bouaghi, Oum El Bouaghi, Algeria
• 2 University of Bisha, Bisha, Saudi Arabia
• 3 Department of Management Administration, Faculty of Economics, University of White Nile University, Kosti, Sudan

e-Learning is a key strategy in the course of higher education to improve the results of the educational process and stimulate student motivation. The COVID-19 pandemic imposed on Algerian universities to adopt e-Learning systems to search for effectiveness and efficiency of academic approaches. This paper seeks to remedy these problems by analyzing the impact of e-Learning systems on student motivation and outcomes. A mixed-method approach was used in the data analysis. We conducted the study as a survey, with data being gathered via questionnaires distributed to 398 students. The questionnaire includes open questions that were qualitatively analyzed using content analysis with Nvivo, besides Likert scale questions were quantitatively analyzed and modeled using Structural equation modeling (SEM) with Amos to accomplish the path analysis of the research model. The results of the study showed that student motivation (Attention, Relevance, Confidence, and Satisfaction) and student outcomes (knowledge, skills, and attitudes) are significantly affected by e-Learning systems (Technical and electronic requirements, personal requirements, perceived value, and credibility of e-Learning). The key findings are discussed, and they provide recommendations for future research.

Introduction

e-Learning has become an inevitable strategy for higher education institutions, especially with the emergence of the COVID-19 pandemic, which was imposed different configurations of learning and teaching processes toward focusing more on: blended learning, distance learning, online learning, and smart learning, e.g., Adnan and Anwar (2020) , Claps et al. (2020) , Çubukçu and Akturk (2020) , and Fadillah et al. (2020) .

Therefore, several research groups like Almaiah et al. (2020) , Al-Okaily et al. (2020) , Alqahtani and Rajkhan (2020) , Radha et al. (2020) , and Shahzad et al. (2020) have been working on the trend of e-Learning to consider the COVID-19 pandemic and its effects. Shahzad et al. (2020) measured the effects of COVID-19 in e-Learning on higher education institution students.

Al-Okaily et al. (2020) extracted the positive effects of e-Learning on student intention to use e-Learning systems during the COVID-19 pandemic. Radha et al. (2020) reflect in their study on the impact of e-Learning, student interest in using e-Learning resources, and their performance, where e-Learning is subject to challenges to achieve its goals ( Almaiah et al., 2020 ). Also, focus on the Critical Success Factors in this pandemic, especially from the managerial perspective ( Alqahtani and Rajkhan, 2020 ).

This provides a solid foundation on which future research can be built, regarding the effects and credibility of e-Learning on higher education and the effectiveness of e-Learning systems in improving student motivation and outcomes. Up to now, several studies have tested these effects ( Harandi, 2015 ; Fryer and Bovee, 2016 ; Yilmaz, 2017 ), and confirmed the role of e-Learning in engendering student satisfaction and motivation.

Islam (2011) , Saba (2012) , and Logan et al. (2021) established the implications of e-Learning systems to facilitate student learning and outcomes. However, recent studies have tested the likely impact of e-Learning on university students during COVID-19 ( Alawamleh et al., 2020 ; Sankar et al., 2020 ; Wargadinata et al., 2020 ).

Algerian universities have also turned toward e-Learning as a strategy for developing educational curricula and teaching processes and forming a bet that guarantees the success of education in light of crises similar to the COVID-19 crisis ( Guessar, 2020 ). Research in this area is of great interest and with a very active research community, in Algeria, many researchers were interested in e-Learning issues and their effects on university students before the COVID-19 crisis ( Zine El Abiddine, 2013 ; Aoued, 2016 ) and during this pandemic ( Guessar, 2020 ; Zermane and Aitouce, 2020 ).

A closer look at the literature reveals many gaps and shortcomings. Firstly, most Algerian studies in the field of e-Learning have only focused on measuring the general effects of e-Learning, and have not been able to check its effects on student motivation and outcomes; this is what you should focus on ( Abou El-Seoud et al., 2014 ). Secondly, this particular problem (Measuring the effects of e-Learning on student motivation and outcomes) was not sufficiently addressed in light of the COVID-19 pandemic. Finally, previously published studies on this trend are not consistent, most of them focused on measuring general effects, or measuring special effects (student motivation and outcomes), but with a purely quantitative approach ( Radha et al., 2020 ; Soni, 2020 ).

However, this method of analysis has several limitations; the most important is not determining the potential effects of e-Learning on qualitative variables, especially when we discuss students’ motivation and outcomes ( Heller and Sottile, 1996 ; Saeed and Zyngier, 2012 ).

The problem of the study is to identify the effects of e-Learning and its contribution to stimulating Algerian students’ motivation and enhancing their educational outcomes during the COVID-19 pandemic, by relying on quantitative and qualitative methods. What is known to researchers as mixed methods? ( Johnson et al., 2007 ; Denzin, 2010 ; Creswell, 2011 ).

Then, this major problem includes two sub-problems: The first is to measure the effects of e-Learning on stimulating students’ motivation, like the following previous studies ( Barolli et al., 2006 ; Lanzilotti et al., 2009 ; Harandi, 2015 ; Fryer and Bovee, 2016 ; Govorov et al., 2016 ; Yilmaz et al., 2017 ). The second is to determine the effects of e-Learning in enhancing student outcomes, approximating the following prior studies ( Islam, 2011 ; Saba, 2012 ; Koraneekij and Khlaisang, 2015 ; Logan et al., 2021 ).

Theoretical Background

Many high education institutions have attempted to encourage e-Learning in response to the requirement of educational continuity in light of COVID-19. This raised a question about the feasibility and effectiveness of this process under this circumstance, particularly for universities unfamiliar with this learning style.

Alismaiel (2021) defined e-Learning as a method of learning that is based on formalized education and employs online databases or resources. For Looi (2021) , e-Learning is more than making teaching materials digital, it is also associated with various psychological and social factors. In e-Learning, every aspect of the educational process, from implementation to assessment, is aided by technology, including media and learning support tools ( Harahap and Fitri, 2021 ).

Furthermore, the usage of e-Learning enables educators to improve the quality of education by using quick replenishing global educational resources. Also, by increasing the amount of autonomous work required of students while studying the content ( Sandybayev, 2020 ).

The information and communication technology advancements have permitted new learning ways:

– Technical and electronic requirements: The technology requirements of e-Learning investigate concerns of technology infrastructure in the e-Learning environment, infrastructure planning, hardware, and software ( Pislae-Ngam et al., 2018 ).

– Personal requirements: Implementing e-Learning into a traditional university’s teaching design is a lengthy and challenging process requiring a systematic approach ( Sandybayev, 2020 ). However, due to the Corona epidemic and the circumstances surrounding the forced shutdown, many universities were obliged to transition into e-Learning, to their lack of preparation. Tan (2020) stated that, although the teaching faculty successfully transitioned from traditional teaching techniques to online learning, the consequences were unclear; the majority of the teaching staff were unprepared for online instruction and were compelled to adjust to the transition as a result of the crisis. The personal dimension relates to the extent of training or willingness to use information technology, especially for students. Student perceptions of e-Learning activities via computer use are referred to as “learner attitudes.” For instance, when students are not intimidated by the complexity of using computers, will result in more contented and productive learners.

– The perceived value of e-Learning: The term “perceived value” in the e-Learning context refers to students’ overall appraisal of the usefulness of learning based on their views of what they receive and what they provide in return ( Faqih, 2016 ). His study also conducted that perceived value elements positively influence students’ intention to adopt and use e-Learning technologies.

This study aims to provide a conceptual theoretical framework based on previous studies and its adoption as a model to be tested in Algerian universities, this model includes three variables: e-Learning, student motivation, and student outcomes.

e-Learning and Student Motivation

Motivation is a vital aspect of any educational process, especially as it relates to e-Learning. There is no single definition of motivation. Espinar Redondo and Ortega Martín (2015) stated that the existence of such a wide range of concepts demonstrates the difficulty in describing motivation. So, motivation can be defined as what inspires students to dedicate time to a certain task freely. Also, as their attitudes and feelings about the activity, as well as how long they remain committed to the task ( Filgona et al., 2020 ).

According to Keller (2010) , the study of motivation is difficult because there are so many motivating ideas, concepts, and theories produced to explain its different elements and the interplay of environmental, cultural, and personal factors. Keller (1983) introduced the ARCS model of four categories (Attention, Relevance, Confidence, and Satisfaction) as a tool quickly understand the main parts of human motivation, especially in learning motivation, and how to stimulate and keep motivation in each of the four areas ( Keller, 2010 ).

The first step in this model is to maintain learners’ curiosity and interest (Attention), the second is to convince the learner that his or her experience is personally meaningful (Relevance), the third step is to convince the learners that they can understand the material and accomplish an activity or a task (Confidence), and the last step is to be sure that learners feel good about what they did or how it worked out (Satisfaction; Keller, 2010 ). The increasing number of research shows the positive effects of using an effective e-Learning process and student motivation and participation ( Herath et al., 2021 ).

H1 : There is a direct and significant impact of e-Learning on student motivation in Algerian universities

e-Learning and Student Outcomes

According to Prøitz (2010) , there is considerable debate and ambiguity around the concept of learning outcomes and the widely accepted definition is concentrated on whether learning and its outcomes can and must be expressed in comprehensive, consistent, pre-determined, and quantifiable terms, or open and flexible ones with limited measurement options. For ( Maher, 2004 ) the term “learning outcomes” is about the student behavior changes because of a learning experience. This change can occur in terms of knowledge, skills, and attitudes. It has long been a concern of researchers and educators that learned motivation has a direct correlation to student progress and intended results ( Esra and Sevilen, 2021 ). For instance, a study ( Sandybayev, 2020 ) conducted that e-Learning is more successful than traditional teaching methods in supporting students enrolled in business courses. In their meta-analysis, Cook et al. (2008) claimed that internet-based learning contributes to knowledge acquisition and skill development compared to non-Internet educational approaches. Also, George et al. (2014) found that online Learning seems to be more successful and can cause an improvement in student knowledge, skills, and attitudes.

H2 : There is a direct and significant impact of e-Learning systems on student outcomes in Algerian universities

Student Motivation and Student Outcomes

Several studies have found that student motivation has a direct impact on student outcomes. In this regard, the literature claims that there is a correlation between these two variables, and this study backs up that notion ( McKenzie and Schweitzer, 2001 ; Sankaran and Bui, 2001 ; Fini and Yousefzadeh, 2011 ; Richardson et al., 2012 ; Azizoğlu et al., 2015 ).

H3 : There is a direct and significant impact of student motivation on student outcomes in Algerian universities

e-Learning, Student Motivation, and Outcomes

As discussed above, e-Learning has a significant influence on student outcomes. However, this interaction cannot take place unless there is a motivating factor involved. Learner motivation has long been a focus for researchers and educators since it is linked directly to student progress and the expected outcome ( Esra and Sevilen, 2021 ). Numerous studies have demonstrated that increasing student motivation to learn improves their academic performance and outcomes. Therefore, the relationship between e-Learning and student outcomes is mediated by motivation.

H4 : the relationship between e-Learning and student outcomes is partially mediated by motivation in Algerian universities

Research Framework

The research framework is shown in Figure 1 .

Figure 1 . Research framework.

Materials and Methods

To verify the validity of the theoretical framework that links e-Learning systems, Student motivation and student outcomes, we carried out a mixed approach, also known as mixed models, mixed methods and pragmatism ( Creswell and Creswell, 2005 ; Cohen et al., 2017 ). A combination of analysis advantages of quantitative and qualitative was attained in this approach.

Quantitative Data and Sample Selection

This study has been conducted to identify the impact of e-Learning Systems on Student Motivation and Outcomes in Algerian universities. The study was conducted in the form of a survey, with data being gathered via a structured questionnaire distributed among the students at all levels. The initial sample consisted of 400 students, 398 questionnaires were collected with an estimated response of 99.5%, which is very acceptable, according to Sekaran and Bougie (2019) . A snowball sample (non-probability sample) related to network sampling was chosen because of the expected difficulty of obtaining the lists of all Algerian students ( Handcock and Gile, 2011 ).

The questionnaire contained constructs to be measured for quantitative analysis. Construct measurements items were expressed according to a five-point Likert scale that was defined as follows: 1 = strongly disagree, 2 = disagree, 3 = medium agree, 4 = agree, and 5 = strongly agree. The questionnaire included three major constructs in addition to demographic data: e-Learning systems which have three dimensions (Technical and electronic requirements, personal requirements, perceived value of e-Learning, or credibility of e-Learning), Student motivation contains four dimensions [Attention, Relevance, Confidence, and Satisfaction, the ARCS model developed by Keller (1987) ], student outcomes which contains three dimensions (Knowledge, Skills, and Attitudes).

Reliability and validity were calculated using Cronbach’s alpha and Guttman split-half, it was performed via SPSS software (version 25). Table 1 shows the validity and reliability coefficient of the questionnaire constructs.

Table 1 . Validity and reliability statistics.

Table 1 provides the summary statistics for Validity and Reliability; it shows that the reliability coefficients (Cronbach’s Alpha) are 0.919, 0.865, 0.924, and 0.909 for the questionnaire, which is within the acceptable limit according to Bland and Altman (1997) . It presents also that the Validity coefficients (Guttman split-half) are 0.828, 0.646, 0.881, and 0.657 for the questionnaire, which is within the allowed range according to Jackson (1979) . This indicates that the questionnaire employed in this study is suitable for conducting research and drawing conclusions.

Qualitative Data

According to Geer (1988) , survey researchers frequently use open-ended questions to gauge public opinion, this requiring respondent, either vocally or in writing, to construct and present their answers. Predefined categories of responses are not guided in a particular direction ( Züll, 2016 ). This contributes to obtaining qualitative data for the analysis of quantitative results. Many academics believed that triangulation (multi-method approaches) is typically a strategy for boosting research validity and reliability or evaluating findings ( Seale, 1999 ; Stenbacka, 2001 ; McMillan and Schumacher, 2010 ).

The study uses qualitative analysis to gain insights into e-Learning systems, student motivation, and outcomes. Qualitative data were collected from three open-ended questions asked in the questionnaire, first related to e-Learning systems (Are you in accord with the policy of the Algerian Universities for e-Learning? If yes state the reasons, if no mention the reasons also), second about student motivation (Are there clear reasons that motivate or demotivate you to learn, succeed, and achieve your university goals? Is e-Learning considered one of these reasons?). Third about student outcomes (If you want one of the students who achieved satisfactory or unsatisfactory results, please state the reasons? Is e-Learning considered one of these reasons?).

Methods and Analysis Approaches

Quantitative methods.

We have used structural equation modeling (SEM) through IBM SPSS Amos 25 to assess the relationships in the research framework and test the hypothesis. Nachtigall et al. (2003) indicate that the comparison of the model to empirical data is the main feature of SEM. This comparison generates so-called fit statistics, which evaluate the model’s fit with the data. This method or co-variance based structural equation modeling (CB-SEM) requires three conditions ( Lowry and Gaskin, 2014 ). Suitable for confirmatory studies and the model must be precisely delimited between the variables, appropriate for large samples (the study’s sample size is greater than 200, with 398 questionnaires gathered), requires a normal distribution of the data shown in Table 2 .

Table 2 . Tests of normality.

A significant divergence from normality, according to West et al. (1995) , is defined as an absolute skewness value >2, and an absolute kurtosis (proper) value >7. Table 2 shows that all of the research variables’ absolute values are less than 2, for skewness and less than 7 for kurtosis, indicating that the data follow a normal distribution.

Qualitative Methods

NVivo is a software program that can be used to save, manage, and analyze qualitative data and open-ended questions ( Edwards-Jones, 2014 ). Visualization techniques (thematic analysis, cluster analysis, and cognitive mapping were used to link three variables: e-Learning systems, student motivation, and student outcomes, to confirm the study model qualitatively and test the degree of its agreement) and thought experiments can also help to clarify what might be useful questions ( Jackson and Bazeley, 2019 ).

Results of Study

To ensure hypothesis testing and study model the results of the quantitative and qualitative studies are given and compared in this section.

Descriptive Statistics

The table below illustrates the summary descriptive statistics for the study sample.

Table 3 presents a summary of the study sample’s demographic factors, where it appears that most of the respondents are female (247 with a percentage of 62.1), with an age from 21 to 30 (248 with a percentage of 62.3), and most of them are Ph.D. students (246 with a percentage of 61.8). This explains the nature of the sample and the respondents who answered the questionnaire. Diment and Garrett-Jones (2007) confirm that demographic characteristics affect the answers and study variables, whose statistics are presented in Table 4 .

Table 3 . Descriptive statistics of the study sample.

Table 4 . Descriptive statistics of study variables.

The majority of respondents were inclined to “medium agree” for e-Learning systems (mean = 2.7261) and student outcomes (mean = 2.8162), but “disagree” for student motivation (mean = 2.7261), with a weak dispersion of the three variables based on the SD. This is explained by Algerian universities’ recent embrace of e-Learning systems in response to the COVID-19 pandemic, as well as their lack of emphasis on interactive e-Learning, which boosts student motivation and enhances student outcomes ( Abdelouafi, 2020 ; Zina, 2021 ).

Correlation Results

The correlation matrix between study variables and constructs is shown in the table below.

The results of the correlation analysis are set out in Table 5 , it appears that all correlation coefficients are significant at 0.01 except for the relationship between knowledge and learning systems which seemed with a weak correlation according to Schober et al. (2018) , as they are confined to 0.10–0.39, and this is explained by the dependence of Algerian universities on traditional learning (in-person learning) and the recent integration of the e-Learning systems in a way that greatly affects students motivation and outcomes ( Djoudi, 2010 ; Abdelouafi, 2020 ; Guemide and Maouche, 2021 ; Zina, 2021 ).

Table 5 . Correlation matrix.

SEM Results

Thom (1983) indicates that path analysis is a powerful tool for conceptualizing research and connecting theory to the “real world.” Therefore, this technique was used in our study to find direct and indirect links between variables to test the study’s hypotheses and model in the reality of Algerian universities. The structural model’s outputs are shown in the figure below.

According to Browne and Cudeck (1992) , the fit indices of the path model are attained; therefore, the relative chi-square value is less than 5 (3.964), indicating that the suggested model in the study is consistent with the real data. The values of the normed-fit index (0.935), comparative fit index (0.950), and Tucker Lewis index (0.932) are all very close to one, indicating that the study hypothetical model is far from zero (which assumes no relationship between the study variables), as well as a value of RMSEA is 0.086, clearly showing a match between the hypothetical model and the real data. This all leads us to accept the Research framework ( Figure 1 ), as well as the hypotheses which are listed in the table below.

The findings of the entire latent construct are presented in Table 6 . The first step is to determine whether our study hypothesis is valid or not. Value of p is regarded as significant if it is less than 0.05. The data, in particular, point to rejecting the null hypotheses and accepting the alternative hypotheses (H 2a , H 2b , H 3a , H 3b , H 3c , and H 4 ), on the other hand, the null hypotheses are accepted and the following alternative hypotheses are rejected (H 1a , H 1b , H 1c , and H 2c ). In this situation, six of the 10 study hypotheses are significant with the acceptance of the study model by looking at the goodness of fit in Figure 2 .

Table 6 . Direct and indirect effects in structural model.

Figure 2 . Structural model.

In terms of direct effects, personal requirements and the perceived value of e-Learning have a significant impact on student motivation and outcomes, and student motivation has a considerable impact on student outcomes. In terms of indirect effects, the perceived value of e-Learning has a significant impact on enhancing student outcomes through student motivation. On the contrary, we find that technical and electronic requirements have no significant effect on student motivation and outcomes for direct effects and that technical and electronic requirement. In addition, personal requirements have no significant effect on improving student outcomes through student motivation for indirect effects.

Qualitative Results

According to Braun and Clarke (2006) thematic analysis is a method for describing qualitative data, but it also incorporates interpretation in the processes of selecting codes and creating themes and respondents’ evaluations of Algerian universities’ e-Learning experience through NVivo12 outputs:

Figure 3 provides an overview of respondents’ attitudes regarding e-Learning systems, student motivation, and outcomes in Algerian universities. According to the open-ended questions, there are two trends. Firstly, the positive view; which constituted 52% of the respondents in e-Learning who agree with the policy of the Algerian Universities for e-Learning, and it also constituted 66.33% for the student’s motivation, and the belief that e-Learning is one of the reasons for improving students motivation, and 48.40% for the student’s outcomes among the respondents who supposed that e-Learning was a reason to enhance their outcomes. Secondly, the negative view in which the respondents believe the opposite and prefer in-person learning, which constituted 48% for e-Learning systems, 33.77% for the student motivation, and 51.60% for student outcomes. This is explicated by two features: the importance of e-Learning systems in Algerian universities, and the emphasis on blended learning in improving students’ motivation and results.

Figure 3 . Matrix query of thematic analysis.

Eden, Jones, and Sims define cognitive mapping as a modeling technique that aims to show ideas, beliefs, values, and attitudes. Their relationships with one another are in a form that is amenable to study and analysis ( Northcott, 1996 ). According to this approach, Figure 4 shows the relationship between the study variables based on the cluster analysis results:

Figure 4 . Cluster analysis of study variables.

Figure 4 shows that respondents believe there is a clear relationship between e-Learning systems and enhancing student motivation and outcomes. The coefficients of correlation indicated in the table below can be used to estimate this relationship.

Table 7 shows that there is a strong relationship between e-Learning systems and student motivation in the pop-up, with a correlation coefficient of 0.984872, followed by a strong relationship between e-Learning systems and student outcomes in the second degree, with a correlation coefficient of 0.885074, and a strong relationship between the student’s motivation and outcomes in the third degree, with a correlation coefficient of 0.984872, followed by the relationships between other variables that appear with weak to moderate correlation coefficients. This is explained by several aspects: first, there is a strong relationship between the three study variables; second, there is a relationship between the variables and their inverse (for example, talking about motivation leads the interviewer to discuss demotivating); and third, there is a very weak relationship between e-Learning systems and failure to enhance students motivation and outcomes.

Table 7 . Pearson correlation coefficient.

Discussion and Implications

This paper is a modest contribution to the ongoing discussions about the impact of e-Learning systems (technical and electronic requirements, personal requirements, and perceived value of e-Learning) in enhancing student motivation (attention, relevance, confidence, and satisfaction) and student outcomes (knowledge, skills, and attitudes) in Algerian universities. The author’s attention was focused on three major problems, the first of which is the impact of e-Learning systems on student motivation, the second is the impact of e-Learning systems on student outcomes, and the third is the impact of the student’s motivation on their outcomes, using the mixed method, quantitative approach, or path analysis of the data of 398 questionnaires distributed to Algerian university students with Amos, also qualitative analysis of open-ended questions in this survey using NVivo.

The originality of our method arises from the fact that we linked three key elements in Algerian higher education: e-Learning systems, student motivation, and student outcomes, where the relationship between these variables was measured using the mixed method (quantitative and qualitative approaches). The quantitative findings revealed that personal requirements and the perceived value of e-Learning have a significant effect on students’ motivation and outcomes. In addition, to a significant effect on the student’s motivation for their outcomes. On the other hand, there is an indirect significant effect of the perceived value of e-Learning on student outcomes through student motivation. The qualitative findings validated the usefulness of e-Learning systems in motivating the students and increasing their outcomes, especially when used in conjunction with an in-person learning system.

These results concur in good agreement with other studies which studied the three basic problems of this research paper. First, regarding the effect between e-Learning systems and student motivation, at the quantitative level, Rovai et al. (2007) confirmed that e-Learning students had higher intrinsic motivation (to know, do things, and feel stimulation) than on-campus students who attend face-to-face sessions, Abou El-Seoud et al. (2014) emphasized that the usage of interactive components of e-Learning, such as the Moodle e-Learning platform, boosts undergraduate students motivation in Egyptian universities, this is also consistent with the study of Harandi (2015) , which confirmed that students are more likely to be motivated when using e-Learning, As for Beluce and Oliveira (2015) , through their analysis of the data of 572 students from the Brazilian state of Paraná, they confirmed that for educators and psychologists who work with learning, the data demonstrated considerable rates of autonomous motivational behavior, Goh et al. (2017) analyzed 670 questionnaires distributed to Malaysian universities students using exploratory factor analysis and regression analyses, this research shows how important it is for university administrators and teachers to develop e-Learning courses that maximize student’s motivation. This is in agreement with Sandybayev (2020) who established that the use of an e-Learning approach, particularly in the business school learning environment, as well as the active use of interactive features such as BBL, enhances motivation.

At the qualitative level, there are several studies whose results agree with the results of our study, the most important are: Yang and Cornelius (2004) who asserted that students have a positive experience were found to be flexibility, cost-effectiveness, electronic research availability, simplicity of connection to the Internet, and a well-designed class interface. Shroff et al. (2007) suggested that a pedagogically driven portfolio of learning activities be used, including well-selected and integrated audio, video, and data technologies in global e-Learning situations to enhance student motivation, Gustiani (2020) used thematic analysis of interviews and discovered students motivation for e-Learning was influenced more by their desire to gain new skills and their delight of trying out new learning methods.

All of these investigations back up the conclusion of our research paper, but it is not a widely accepted view. On the one hand, we may discover that the presence of learning affects the student’s motivation; Francis et al. (2019) found that while e-Learning and face-to-face students may differ in academic outcomes, they do not differ in motivation or student characteristics. On the other hand, we find that e-Learning does not stimulate student motivation in some environments and for some students, as confirmed by Esra and Sevilen (2021) who found that students believe e-Learning hurts their motivation due to a lack of social connection, a mismatch between expectations and material, organizational issues, and the organization of the learning environment.

Second, regarding the effect of e-Learning systems on student outcomes, several studies confirmed the findings of our research paper, Eom and Arbaugh (2011) emphasize that student outcomes improve through e-Learning. At the level of quantitative results, using PLS-SEM Islam (2013) confirmed that e-Learning adoption antecedents (ease of use, utility, enjoyment, system quality, information quality, service quality, self-efficacy, usability, and playfulness) have an impact on e-Learning adoption outcomes (learning assistance, community building assistance, and academic performance). Goh et al. (2017) confirmed that e-Learning courses maximize student learning outcomes in Malaysian universities; Ritonga et al. (2020) also concluded that e-Learning has an impact on student learning outcomes. Baber (2020) confirmed that the factors–interaction in the classroom, course structure, instructor knowledge, and facilitation in e-Learning systems are positively influencing students’ perceived learning outcomes.

Other investigations support the findings of our study on a qualitative level concerning the impact of e-Learning on student outcomes. Blackmon and Major (2012) explored that some students were satisfied with their online courses and enrolling in an online program related to their jobs was very beneficial for academic outcomes. The thematic analysis used by … that the access and use of technological resources in classrooms, implementing the e-Learning methodology the COVID-19 lockdown affect the academic performance and student outcomes.

The issue of the consistency of the impact of e-Learning on student outcomes persists. The e-Learning methodology may affect student outcomes if it is used as a pillar of attendance learning. Or what is known as blended learning? This was confirmed by Kintu et al. (2017) who showed that blended learning design features (technology quality, online tools, and face-to-face support) and student characteristics predicted student outcomes. In addition, e-Learning may not affect the results of some students who are not skilled in using them or who do not have the requirements, including an internet connection. This is what was confirmed by Agbejule et al. (2021) , who showed that there are several barriers to the success of e-Learning, the most important of which are instructional concerns, lack of social connection, type of educational program, and geographical area.

Finally, multiple investigations have supported the conclusions of our research work about the effect of student motivation on student outcomes. In this regard, this study backs up the literature’s claim that there is a link between motivation and student outcomes ( McKenzie and Schweitzer, 2001 ; Sankaran and Bui, 2001 ; Fini and Yousefzadeh, 2011 ; Richardson et al., 2012 ; Azizoğlu et al., 2015 ). Numerous studies have demonstrated this relationship using quantitative and qualitative approaches, for quantitative methods, Goodman et al. (2011) established that there is a high association between intrinsic motivation, extrinsic motivation, and academic performance and outcomes using Pearson correlation coefficients for empirical results from 254 commerce faculty students the University of the Western Cape ( Ferreira et al., 2011 ). Also proved that intrinsic motivation positively and significantly influences perceived learning in the course using the structural equation model.

Similarly, Nur’Aini et al. (2020) demonstrated that the learning motivation of students significantly positive impact their learning outcomes using simple linear regression to analyze the data of a sample of 1,125 students.

For qualitative methods, in their qualitative study, Saeed and Zyngier (2012) arrived at several conclusions, the most notable of which is that extrinsic motivation worked to foster ritual engagement in students, but intrinsic motivation aided true student involvement in learning. Students with both levels of motivation engaged in their learning in a variety of ways.

Because of the differences between the students and the academic environment, these results are not always correct. Martens et al. (2004) , for example, found that students with high intrinsic motivation did not perform better in class in their studies. In addition, Orhan Özen (2017) found that motivation has a low-level positive effect on student achievement in a meta-analysis of 205 studies. Similarly, Howard et al. (2021) found a link between motivation and poor outcomes. As a result, the study has several research limitations.

The results in this study depend on at least four limitations. First, the study did not evaluate the alterations of the relationship between e-Learning systems, student motivation and outcomes, from one university to another, from the academic environment to another, and from students to others. Also, the variances arising from the differences in the professors’ viewpoints may need a meta-study that collects the results of several studies in different environments. Second, one question still unanswered is whether student motivation and outcomes are more influenced by e-Learning than face-to-face learning or vice versa, or by blended learning, this may need another empirical study. Third, the most important limitation lies in the fact that e-Learning is imposed and inevitable during COVID-19, so the degree of its obligation may affect the outcomes and student motivation, either positively or negatively, this needs to compare to the results before and after the pandemic. Finally, to measure the relationship between the study variables, we may need data from a larger sample, and we may need to use other statistical methods. Especially, the analysis of variance. These limitations are considered future research trends.

The main objective of this research was to look into the effects of e-Learning systems on motivating Algerian university students and improving their educational outcomes during COVID-19. It focused on the relationship between three key variables: e-Learning systems (technical and electronic requirements, personal requirements, and perceived value of e-Learning), student motivation (attention, relevance, confidence, and satisfaction), and student entrepreneurship (knowledge, skills, and attitudes). The researchers accomplished this objective by analyzing the data from 398 questionnaires issued to Algerian university students to provide a set of quantitative and qualitative outcomes.

Summing up the quantitative results, it can be concluded according to the correlation matrix that there is a positive significant correlation between e-Learning systems and student motivation, and there is a positive significant relationship between student motivation and student outcomes. According to the SEM results or path analysis model, personal requirements and the perceived value of e-Learning have a significant effect on student motivation and outcomes. Also, student motivation has an indirect significant effect on the perceived value of e-Learning on student outcomes.

The qualitative results obtained show that student positive attitudes regarding e-Learning systems are more than negative ones, and this positively affects student motivation and outcomes according to the thematic analysis. Using cognitive mapping, researchers also demonstrated the strong relationship between e-Learning systems, student motivation and student outcomes, and substantially e-Learning systems affecting student motivation and increasing their outcomes, especially when used in conjunction with an in-person learning system.

The studied model is considered very important in Algerian universities, especially in light of their transition to e-Learning during COVID-19, where university administrators, leaders, and policymakers in the Ministry of Higher Education and Scientific Research can benefit from the study findings on several levels, including determining the credibility of e-Learning in terms of motivating students and improving their outcomes. Second, looking for successful e-Learning curricula that raise student motivation to study, and third, looking for ways to improve student motivation before looking for ways to improve their achievements. Professors and students might consider this when looking for ways to improve student results.

Based on the promising findings presented in this paper, work on the remaining issues is continuing and will be presented in future papers. The next stage of our research will be the study of the relationships between e-Learning systems, students motivation, and student outcomes: a meta-analysis. Several other questions remain to be addressed, the differences between the effects of e-Learning and face-to-face learning on student motivation and outcomes, and the effects of blended learning on student motivation and outcomes. More experiments will be needed to verify the impact of COVID-19 obligations on the teaching and learning process and student outcomes.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author.

Author Contributions

Study design, data collection and analysis, and manuscript editing and writing were all conducted by FY, RA, KC, and SB. All authors contributed to the article and approved the submitted version.

The study is funded by the General Directorate of Scientific Research and Technological Development, Ministry of Higher Education and Scientific Research, Algeria.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors would like to thank the reviewers and the editor for their insightful comments and suggestions.

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Keywords: e-Learning systems, student motivation, student outcomes, COVID-19, mixed-method approach

Citation: Yahiaoui F, Aichouche R, Chergui K, Brika SKM, Almezher M, Musa AA and Lamari IA (2022) The Impact of e-Learning Systems on Motivating Students and Enhancing Their Outcomes During COVID-19: A Mixed-Method Approach. Front. Psychol . 13:874181. doi: 10.3389/fpsyg.2022.874181

Received: 11 February 2022; Accepted: 14 June 2022; Published: 29 July 2022.

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*Correspondence: Said Khalfa Mokhtar Brika, [email protected]

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Musculoskeletal pain is a major concern in our life due to its negative effects on our ability to perform daily functions. During COVID-19 pandemic, several countries switched their teaching programs into e-learning, where students spend long hour using electronic devices. The use of these devices was associated with several musculoskeletal complains among the students. The aim of this study is to evaluate the different body aches associated with e-learning on university students. The subjects of this study were students from An-Najah University in Palestine. 385 questionnaires were filled using Google forms questionnaire and all the subjects were using e-learning due to COVID-19 pandemic. Our study showed that a large percentage of participants used electronic devices for e-learning during the pandemic. The Duration of these devices use was correlated with duration and degree of pain, and associated with the difficulty in ability to perform several daily activities. Furthermore, most of the students used the sitting position with supine bent forward during the device usage. Thus, the university students that participated in this study had an increase in body aches during the e-learning process, and the aches duration and severity increases if the duration of electronic devices usage increase.

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Introduction

Around 20% of adults worldwide suffers from musculoskeletal system pain 1 . The impact of musculoskeletal disorders is particularly highlighted in the workplace setting, where they contribute substantially to annual illness and injury costs and reduced productivity 2 .

Several studies have been performed to evaluate the possible harmful effects of certain office work on general health. The results of these studies showed that neck 3 and lower extremity pain 4 may be associated with sitting for long periods at work, and upper extremity problems 5 may be associated with computer use. Moreover, prolonged sitting can aggravate lower back pain when combined with improper postures (e.g., sitting while leaning forward instead of upright) 6 . Based on the previous studied, the most common musculoskeletal complains among desk-based workers are neck pain, shoulder pain and lower back pain 7 . However, the cause effect relationship is not well established and requires more investigation.

Since the current advance in technology, the use of mobile phones is becoming more common among the populations worldwide. Many studies have been conducted to study the correlation between using mobile phones for texting and both, neck and shoulder pain 8 . In addition, even application of other physical activities, prolonged neck flexion is linked to neck, shoulder, and upper extremity pain 9 . Several studies explained the effect by the static muscular load and prolonged neck flexion along with the lack of support to the arms and the repetitive movement of the fingers, especially when using one hand only 9 , 10 . Furthermore, the position that a person takes during mobile phone utilization can be linked to physical pain associated with mobile use for texting. It is documented that the best position is the sitting position with a straight neck and supporting the forearms with holding the mobile phone with both hands and to use both thumbs 11 , 12 .

During the COVID-19 pandemic, several countries have tried to coop with social isolation and the general lockdown for all educational institution by switching to other forms of learning 13 . And thus, the E-learning methods have been implicated widely all over the world for all generations.

The switch to e-learning was a big challenge to most of the countries, although some countries have already implicated programs before the beginning of COVID-19 pandemic. Several programs and courses in several universities were already taught from a distance using several e-learning methods. However, during the pandemic all the courses were switched to e-learning methods with the students of different ages spending long hours over their laptops, computers, and smart devices. This change in learning methods was associated with several complains among the students like neck, shoulder and back pain. In addition, the transition to e-learning have presented several other challenges like stress and other psychological implications 14 , 15 , 16 , 17 .

In this study we aimed to evaluate the degree of different body aches associated with e-learning on university students and find a link between the most common body posture that are associated with the high negative health outcome on these students.

Characteristics of the subjects

In an attempt to understand the different pain levels caused by e-learning, the questionnaires were distributed to several faculties at An-Najah National University. A total of 385 students were included in the study, the mean age for study participants was 19.91 (SD = 9.8). The sample included 148 men (38.4%) and 237 women (61.6%) (Table S1 ). Most of the participating students were from the faculty of medicine and health sciences (29.6%), followed by faculty of engineering and information technology (28.3%), faculty of educational sciences and teacher training (20%), faculty of Islamic law (6.8%), faculty of economic and financial sciences (5.5%), faculty of science (4.9%). The participation of students from faculties other than the mentioned were minimum (Table 1 ). Concerning the handedness of the subjects, around 90.6% of the subjects were right handedness, while 4.2% had left handedness and 5.2% can use both of their hands (Table S2 ).

Patterns of laptop, computer or tablet use

When questioning the pattern of laptop and tablet usage, our analysis showed that (1.3%) of the participants never using desktop/laptop, 46.8% of the participants used the desktop/laptop daily, 48.8% of them used the computer from (4–6 days), while 3.1% of them used it from 1–3 days (Table S3 ).

In addition, the average daily usage of laptop and tablets was about 8.2 ± 4.2 h, from these hours around 5.9 ± 3.5 were for e-learning use (Table S4 ). Detailed analysis showed that the main purpose of using the desktop/laptop or tablet device was in favor of multiple usage with percent of (42.3%), then for the studying with percent of (35.1%), followed by for watching videos with percent of (8.6%) and for following social media with percent of (7.8%), and for working with percent of (3.4%) and just for gaming with percent of (1.8%) and finally for texting with percent of (1%). Chi 2 value = 469.855 and its significant at level of < 0.001 and the variance was in favor of multiple purposes (Table S5 ).

Further analysis for different gender usage for laptops and tablet in e-learning showed that female tend to have higher hours than males, 6.38 compared to 5.09, p < 0.001.

Upon analysis of the most common sitting position during desktop/laptop usage, 49.9% of the participants that they were sitting on the chair with the Spine slopping forward. However, 17.1% of the participants said that they usually sit on the chair with straight spine, and 14.3% of the participants said that they usually sit with supine position. In addition, 12.7%, 3.6%, and 2.3% of the participants said that they sit on the ground with supine sloping forward, Spine sloping back, and straight supine, respectively. Chi 2  = (346.268 and its significant at level of < 0.001 and the variance was in favor of sitting position on the chair with back slopping forward (Table 2 ).

Furthermore, there was statistical significance in comparing sitting positions for both genders although both male (44.5%) and female (53.2%) students reported the highest percentage in sitting on chair with supine bent forward (Table S6 ).

Pain experience during desktop/laptop usage

Several questions in the questionnaire were asked about some physical pain that could be associated with desktop/laptop usage. Our analysis showed that 48.3% of the study participants had an earlier experience of neck, back and shoulder pain and that the pain was worst after e-learning. However, 8.6% of the participants said that the pain they had in their neck, back or shoulder didn’t change after e-leaning. In addition, 43.1% of the participants said that they have never had any pain before. Chi 2 value = 107.787 and its significant at level < 0.01 and the variance was in favor of the study sample from the first category (Table S7 ).

When questioning the pain site, our results showed that 32.2% of the participants had neck pain, 15.3% had right shoulder pain, 20% had left shoulder pain, 15.1% had back pain, while 17.4% of the participants didn’t have pain at all (Table S8 ).

Regarding the pain frequency among the participants, 5.2% of the participants had pain in one day per week, 14.3% had pain 2 days per week, 17.7% had pain 3 days per week. 15.8%, 10.4% 3.6% and 15.6% had pain in 4, 5, 6 and 7 days per week, respectively. On the other hand, 17.4% of the participants said they don’t have any pain, noting that Chi 2 value was (64.974) and its significant at level < 0.001 (Table S9 ).

Further analysis for the exact duration of the pain showed most participants had pain for 1–6 h per day. Chi 2 value = 453.784 and its significant at level < 0.001 and the variance was in favor of pain duration (1–6 h) (Table 3 ).

In our study, we also questioned the most common timing of the pain. Our results showed that the participants most common timing of the pain was at the night (36.1%), while 9.9% of the participants had pain in the morning, and 13.8% had pain in the afternoon, and 22.9% had pain throughout the day. Chi 2 value was (77.688) and its significant at level < 0.001 (Table S10 ).

On the other hand, analysis of the pain severity was assessed using a 10-degree scale. Chi 2 value confirms that there is a variance between the pain's severity among the participants and the degrees were ranged between 0 to 10 but the most pains severity was from degree (2–8), while (9–10) degrees were less than other degrees (Table S11 ).

To evaluate the effect of the pain associated with desktop/laptop use on the daily activity of the participants, we asked them to assess their ability to perform several daily functions. Our results showed that the mean of difficulties found in case of neck and back is 1.79/4.00 ± 0.65 which is equivalent to low level difficulty on a scale of no, low, moderate, and severe difficulty. However, walk for several miles was ranked first with the mean of 2.02 ± 0.99 and it is of a moderate level on the difficulty scale. In addition, standing up for 20 to 30 min ranked second with mean of 1.92 ± 0.94 and it is of low level on the difficulty scale. On the other hand, walking for short distances was ranked last with mean of 1.41 ± 0.67 which is also equivalent to low level on the difficulty scale (Table 4 ).

Pain experience during e-learning

Upon analyzing predictors for pain severity, we found that the duration of desktop/laptop usage for e-learning was significantly associated with pain duration (p < 0.01) with Pearson correlation of 0.146 for duration of use (Fig.  1 ).

The correlation between the duration of desktop/laptop or tablet device usage for e-learning and duration of pain.

In addition, our results showed a significant correlation between the duration of desktop/laptop use for e-learning and the severity of the pain among participants (p < 0.001) with Pearson correlation of 0.199 for duration of use (Fig.  2 ).

The correlation between the duration of desktop/laptop or tablet device usage for e-learning and pain severity.

Furthermore, our study showed that duration of desktop/laptop usage for e-learning was correlated significantly with increased difficulty of getting out of bed (p < 0.001, Fig.  3 A), sleeping through the night (p < 0.01, Fig.  3 B), turning over in bed (p < 0.001, Fig.  3 C), standing for 20–30 min (p < 0.5, Fig.  3 D), bending over (p < 0.01, Fig.  3 E) and walking for several miles (p < 0.001, Fig.  3 F) with Pearson correlation of 0.177, 0.169, 0.233, 0.129, 0.134 and 0.184, respectively.

The correlation between the duration of desktop/laptop or tablet device usage for e-learning and daily activity difficulties. The correlation between the duration of desktop/laptop or tablet device usage for e-learning and getting out of bed ( A ), sleeping through the night ( B ), turning over in bed ( C ), standing for 20–30 min ( D ), bending over ( E ) and walking for several miles ( F ).

When comparing the duration of desktop/laptop or tablet use with gender, there was a significant correlation between both factors (p < 0.001) with Person correlation of 0.197 (Fig.  4 A). Moreover, a significant correlation was also detected between the gender of the participants and the severity of the pain (p < 0.001) with Person correlation of 0.267 (Fig.  4 B).

The correlation between gender of the participants and the pain duration and severity. The correlation between gender and pain duration ( A ) and the correlation between gender and pain severity ( B ).

The use of electronic devices like desktop/laptop and tablets have increased widely among students during the COVID-19 pandemic due to global shifting in education to e-learning. Our study showed that using desktop/laptop or tablets among students was associated with increased neck and pain and the longer the duration use the more severe the pain. In addition, the pain could affect the normal activity of the students in certain aspects like sleeping, bending over and walking for long distances. Most of the students usually sit on the chair with supine slopping forward during desktop/laptop or tablets.

In our study, the students’ participants were from several faculties at An-Najah National University from both genders. The female participants had greater percentage compared to male in this study. Furthermore, our results showed that females tend to use desktop/laptop or tablets for e-learning for longer duration compared to male, although both of the gender had an almost similar percentage in siting position during desktop/laptop or tablets usage, and the highest percentage sit on the chair with supine slopping forward. However, both pain duration and pain severity were higher in females due to desktop/laptop or tablets usage, this is in accordance to previous studies that showed a higher prevalence of neck pain 18 , 19 in females compared to males. In addition, even at earlier age like school age, female showed higher percentage of back pain compared to male possibly due to psychological factors, female hormone fluctuation, and menstruation 20 .

Concerning the desktop/laptop or tablet usage, our study showed that 46.8% of the participants used these devices daily, and that around 42.3% of the participants used it for several purposes while 35.1% of the participants used it for studying. During desktop/laptop or tablet use, half of the participants sit on the chair with their spine slopping forward. Several previous studies showed a correlation between sedentary life style where the individual spend long time sitting and low back pain 21 , 22 . Furthermore, prevalence of low back pain is high in office worker 23 . All these studies could explain our results since the normal dynamic students’ life has shifted to more sedentary life during COVID-19 pandemic where the students receive most of their education online while sitting in their houses.

Our study also showed that around 50% of the students had an earlier experience of neck, shoulder or back pain, although this pain was worst after e-learning. The frequency and duration of this pain varied among participants, but there was a statistical significant in the pain duration range of 1–6 h per day compared to other groups. Previous studies also showed that during e-learning the student tend to adopt inappropriate postures that can cause pain and musculoskeletal alterations, especially in the upper limbs and spine 24 .

The timing of the pain also varied among participants, but the most common time of pain was at night. This is an important finding because pain at night can affect sleep and some early studies suggest that tiredness, difficulties in falling asleep, waking up at night and other sleep problems can increase the risk of musculoskeletal pains 25 , 26 , and thus, this factor can increase the pain associated with e-learning.

However, our analysis showed that there was a significant correlation between the duration of desktop/laptop or tablet usage for e-learning and the duration and the severity of the physical pain. These results indicate that the longer the time spent for e-learning the highest the duration and severity of the pain. In accordance with these results, a systematic review aimed at evaluating the prevalence and risk factors for musculoskeletal complains associated with mobile handheld device use showed that there is a significant relationship between the duration of smartphone usage and the musculoskeletal complains 27 .

Finally, our study showed that the duration of desktop/laptop or tablet usage in e-learning significantly affected some daily activities of the participants like getting out of bed, sleeping through the night, turning over in bed, standing for 20–30 min, bending over and walking for several miles. Increasing the risk of these daily activities by the pain associated with e-learning is a warning sign for this young group of the society as it can negatively affect their general health and even negatively affect their ability to study.

The results of our study showed several negative health effects on early adulthood aged students due to e-learning methods. Thus, more focus should be directed to these students to improve their general health and decrease the difficulties in their daily life activities that is associated with increased e-learning hours. More strategic help should be provided by universities and health care professionals to guide these students on the best body positions during their e-learning, and to teach them how to improve the flexibility and strength of trunk muscles using series of stretching and resistance exercises for the upper body and lower body.

Materials and methods

The subjects of this study were students from An-Najah University in Palestine. The data collection was from 10/11/2020 to 10/2/2021. During this period, 385 questionnaires were filled using Google forms as a web-based questionnaire (Supplement 1 File). Questionnaires were distributed to students by posting it on their groups on social networks like Facebook. Students from almost all faculties at the university were included. All the subjects were using e-learning teaching approach due to COVID-19 pandemic.

Questionnaire design

At the beginning of the questionnaire, general demographics data including age, gender, and faculty were studied. Later, the general conditions for usage of desktop/laptop or tablet devices including handedness, frequency of use, duration, causality of usage and position during use were studied. Students’ experience of neck, back and shoulder pain associated with e learning use of the previously mentioned devices, including the severity of the pain using the NRS-11 was evaluated; students were asked to rate their pain on a scale from 0 to 10, where zero represents “no pain at all” and 10 represents “the worst pain they have ever experienced”, using whole numbers. At last, the frequency, duration and timing of pain and how bad the pain affected their daily activities were also studied.

Ethical approval

Ethical approval for our study entitled “E-learning and body aches among students in Palestinian University” was obtained from An-Najah National University IRB committee on 27th of October 2020 (OTH 10/2020/21) and all methods were carried out in accordance with relevant guidelines and regulations.

An informed consent was obtained in the first page of the study’s questionnaire, and it was written in Arabic, which is the official language in Palestine, it explained the aims of the study and emphasized the confidentiality of the filled information. Participants were able to withdraw from the questionnaire at any point. No identifying information were obtained through the questionnaire, and all collected data were solely used for statistical analysis.

Statistical analysis

SPSS (version 21.0, Chicago, USA) was used in analysis of the data. Descriptive statistics were used to study the sample. Correlation statistics with Pearson coefficient was used to assess the correlation between duration of use, and both pain duration and severity, and for the assessment of the correlation between gender and duration and severity of the pain. Chi square analysis was also used to test the null hypothesis in some factors. A p value of 0.01 was adopted as a threshold for significance.

Ethics approval and consent to participate

Ethical approval for our study entitled “E-learning and body aches among students in Palestinian university” was obtained from An-Najah National University IRB committee on 27th of October 2020 (OTH 10/2020/21).

An informed consent was obtained in the first page of the study’s questionnaire, and it was written in Arabic, which is the official language in Palestine, it explained the aims of the study and emphasized the confidentiality of the filled information and all methods were carried out in accordance with relevant guidelines and regulations.

Our study showed that the university students that participated in this study had an increase in pain during the e-learning process, and that this pain duration and severity increases if the duration of desktop/laptop or tablet usage increase. This pain can be severe in some students that it affects their ability to perform some of their normal life activities. Our results indicate that these students need help in explaining the best position and daily practices that can decrease their degree of pain.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Yaseen, Q.B., Salah, H. The impact of e-learning during COVID-19 pandemic on students’ body aches in Palestine. Sci Rep 11 , 22379 (2021). https://doi.org/10.1038/s41598-021-01967-z

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The spread of COVID-19 poses a threat to humanity, as this pandemic has forced many global activities to close, including educational activities. To reduce the spread of the virus, education institutions have been forced to switch to e-learning using available educational platforms, despite the challenges facing this sudden transformation. In order to further explore the potentials challenges facing learning activities, the focus of this study is on e-learning from students’ and instructor’s perspectives on using and implementing e-learning systems in a public university during the COVID-19 pandemic. The study targets the society that includes students and teaching staff in the Information Technology (IT) faculty at the University of Benghazi. The descriptive-analytical approach was applied and the results were analyzed by statistical methods. Two types of questionnaires were designed and distributed, i.e., the student questionnaire and the instructor questionnaire. Four dimensions have been highlighted to reach the expected results, i.e., the extent of using e-learning during the COVID-19 pandemic, advantages, disadvantages and obstacles of implementing E-learning in the IT faculty. By analyzing the results, we achieved encouraging results that throw light on some of the issues, challenges and advantages of using e-learning systems instead of traditional education in higher education in general and during emergency periods.

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Introduction

The unexpected closure of educational institutions as a result of the emergence of COVID-19 prompted the authorities to suggest adopting alternatives to traditional learning methods in emergencies to ensure that students are not left without studying and to prevent the epidemic from spreading.

The formal learning system with the help of electronic resources is known as e-learning. Whereas teaching can be inside (or outside) the classrooms, the use of computer technology and the Internet is the main component of e-learning (Aboagye et al. ( 2020 ). The traditional educational methods were replaced by e-learning when the COVID-19 virus appeared because social gatherings in educational institutions are considered an opportunity for the virus to spread. E-learning is the best option available to ensure that epidemics do not spread, as it guarantees spatial distancing despite the challenges and studied figures, which indicate that students are less likely to benefit from this type of education (Lizcano et al. ( 2020 ).

Information and communication technologies (ICTs) offer unique educational and training opportunities as they improve teaching and learning, and innovation and creativity for people and organizations. Furthermore, the use of ICT can promote the development of an educational policy that encourages creative and innovative educational institution environments (Abdullah et al. 2019 ; Altawaty et al. 2020 ; Selim, 2007 ). Therefore, attention is given widely to efforts and experiences related to this type of education. This technology is commonly used by most universities in several developing countries. In an educational environment, there are lots of learning-related processes involved, and great amounts of potential rich data are generated in educational institutions continuously in order to extract knowledge from those data for a better understanding of learning-related processes (Aljawarneh, 2020 ; Lara et al. 2020 ; Lizcano et al. 2020 ).

E-learning is playing a vital role in the existing educational setting, as it changes the entire education system and becomes one of the greatest preferred topics for academics (Samir et al. 2014 ). It is defined as the use of diverse kinds of ICT and electronic devices in teaching (Gaebel et al. ( 2014 ). Most students today want to study online and graduate from universities and colleges around the world, but they cannot go anywhere because they reside in isolated places without good communication services.

Because of e-learning, participants can save time and effort for living in distant places from universities where they are registered, so, many scholars support online courses (Ms & Toro, 2013 ).

Many users of e-learning platforms see that online learning helps ensure that e-learning can be easily managed, and the learner can easily access the teachers and teaching materials (Gautam, 2020 ; Mukhtar et al. 2020). It also helped reduce the effort and travel expenses and other expenses that accompany traditional learning. E-learning reduced significantly the administrative effort, preparation and lectures recording, attendance, and leaving classes. Teachers, as well as students, see that online learning methods encouraged pursuing lessons from anywhere and in difficult circumstances that prevent them from reaching universities and schools. The student becomes a self-directed learner and learns simultaneously and asynchronously at any time. However, there are many drawbacks of e-learning, the most important of which is getting knowledge only on a theoretical basis and when it comes to using everything that learners have learned without applied practical skills. The face-to-face learning experience is missing, which may interest many learners and educators. Other problems are related to the online assessments, which may be limited to objective questions. Issues related to the security of online learning programs and user reliability are among the challenges of e-learning in addition to other difficulties that are always related to the misuse of technology (Gautam, 2020 ; Mukhtar et al. 2020).

Web-based education, digital learning, interactive learning, computer-assisted teaching and internet-based learning are known as E-learning (Aljawarneh, 2020 ; Lara et al. 2020 ; Yengin et al. 2011 ). It is mainly a web-based education system that provides learners with information or expertise utilizing technology. The use of web-based technology for educational purposes has increased rapidly due to a drastic reduction in the cost of implementing these technologies. Nowadays, many universities have recognized the importance of E-learning as a core element of their learning system. Therefore, further research has been conducted to understand the difficulties, advantages, and challenges of e-learning in higher education. These issues have the potential to adversely affect instructors' quality in the delivery of educational material (Yengin et al. 2011 ).

Technology-based E-learning requires the use of the internet and other essential tools to generate educational materials, educate learners, and administer courses in an organization. E-learning is flexible when considering time, location, and health issues. It increases the effectiveness of knowledge and skills by enabling access to a massive amount of data, and enhances collaboration, and also strengthens learning-sustaining relationships. Although e-learning can enhance the quality of education, there is an argument about making E-learning materials available, which leads to improving learning outcomes only for specific types of collective evaluation. However, e-learning may result in the heavy use of certain websites. Moreover, it cannot support domains that require practical studies. The main drawback of using e-learning is the absence of crucial personal interactions, not only between students and teachers but also among fellow students (Somayeh et al. 2016 ). Compared to developed countries, it was found that developing countries face many challenges in applying e-learning, including poor internet connection, insufficient knowledge about the use of information and communication technology, and weak content development (Aung & Khaing, 2015 ). The provision of content such as video and advanced applications is still a new thing for many educators, even at the higher education level in developing countries (Aljawarneh, 2020 ; Lara et al. 2020 ; Lizcano et al. 2020 ).

This study aims to identify issues related to the use, advantages, disadvantages, and obstacles of e-learning programs in a public university by extrapolating the perspectives of students and educators who use this mode of learning in long-lasting unusual circumstances. The research population consisted of students and faculty members at the Faculty of IT at the University of Benghazi. Two types of questionnaires have been distributed to students and instructors. To achieve the expected results, four dimensions are defined, i.e., the extent to which E-learning is used and the benefits, drawbacks, and obstacles to the implementation of E-learning by the Faculty of IT. The descriptive-analytical method is used in the statistical analysis of the results. By evaluating the results, we have obtained promising findings that demonstrate some of the higher education sector's problems, obstacles, and advantages of using the E-learning method. Students believe that based on the study’s results, E-learning contributes to their learning. This reduces the instructor workload, however, and raises it for students. The teaching staff agrees that E-learning is beneficial in enhancing the skills of students, although it needs financial resources and the cost of implementing them is high. Despite the advantages of using E-learning, some of the obstacles to its implementation in Libya include the degradation of the Internet infrastructure that supports these education systems in Libya in general. The high cost of buying the electronic equipment needed and maintaining the equipment, which is unemployed.

The remainder of this paper is organized as follows. Section 2 gives some background and related work about E-learning. Section 3 describes the methodology. Statistical analysis is presented in Sect. 4. Section 5 explains the study outcomes. Finally, Sect. 6 discusses the conclusion of this work and provides some recommendations.

Related work

Several studies have addressed the opportunities and challenges associated with the transition to traditional learning instead of e-learning. One of the main reasons for faltering e-learning initiatives is the lack of well-preparedness for this experience.

A study that aims to examine student challenges about how to deal with e-learning in the outbreak of COVID-19 and to examine whether students are prepared to study online or not is presented in (Aboagye et al. 2020 ). The study concluded that a blended approach that combines traditional and e-teaching must be available for learners. Another study that aims to explore the e-learning process among students who are familiar with web-based technology to advance their self-study skills is described in (Radha et al. 2020 ). The study results show that e-learning has become popular among students in all educational institutions in the period of lockdown due to the COVID-19 pandemic.

A study that aims to investigate the characteristics, benefits, drawbacks and features that impact E-learning has been presented in (Ms & Toro, 2013 ). Some of the demographic features such as behaviors and cultural background impact student education in the E-learning domain. Therefore, for lecturers to design educational activities to make learning more effective, they should understand these features. The study is applied to students in Lebanon and England to assist instructors to understand what scholars expect from the learning management systems.

Analyzing the effectiveness of E-learning for students at the university level has been introduced in (Ali et al. 2018 ). A questionnaire was applied to a sample of 700 students, 94.9% of them are utilizing different e-learning techniques and tools. To measure the reliability and internal consistency of the factors, Cronbach’s alpha test is applied. To take out the variables and to calculate the factors loading in the study, the exploratory feature analysis is applied. The results demonstrate that students support that E-learning is easy to use, saves time, and affordable.

Various predictions of e-learning for educational purposes have been illustrated in (Samir et al. 2014 ). The study aims to show how to keep students motivated in e-learning. The evaluation of student motivations for online learning can be challenging because of the lack of face-to-face contact between learners and teachers. The study shows that one way to increase student’s motivation is by allowing them to complete an online assessment form on motivation. The study suggests five research hypotheses to be inspected to identify which hypothesis should be accepted and which should not.

The strength of the relationship between students’ motivation and e-learning is illustrated in (Harandi, 2015 ). Data was gathered from students at Tehran Alzahra University, and Pearson's correlation coefficient was utilized for data analysis. The outcomes of this study revealed that some points should be considered before using E-learning. However, this study was restricted to one culture, which can limit the generalization of its results.

The study described in (Oludare Jethro et al. 2012 ) showed that e-learning is a new atmosphere for scholars, as it illustrates how to make e-learning more effective in the educational field and the advantages of using e-learning. The outcome of the study showed that the students were willing to learn more with less social communication with other students or lecturers.

A study that aims to highlight and measure the four Critical Success Factors from student insights is described in (Selim, 2007 ). These factors are instructor and student characteristics, technology structure, and university support. The outcomes of the study showed that the instructor characteristics factor is the most critical one followed by IT infrastructure and university support in e-learning success. The least critical factor to the success of e-learning was student characteristics.

The work described in (GOYAL & S., 2012 ) has tried to emphasize the importance of e-learning in modern teaching and illustrates its advantages and disadvantages. Also, the comparison with Instructor Led Training (ILT) and the probability of applying E-learning instead of old classroom teaching was discussed. In addition, the study showed the major drawbacks of ILT in institutions and how using E-learning can assist in overcoming these problems.

The purpose of the study in (Gaebel et al. 2014 ) is to conduct a survey on the varieties of E-learning organizations, skills, and their anticipations for the forthcoming. Blended and online learning are taken into account. Some of the questions related to intra-institutional management, arrangements and services, and quality assurance. The outcomes of the survey showed that from 38 diverse countries and systems, there are 249 organizations broadly conceived the same causes for the increasing use of e-learning.

The study in (Yengin et al. 2011 ) illustrated that the most vital role in the e-learning design outlook is online lecturers. As a result, considering the issues impacting lecturers’ performance should be taken into the account. One of the features that impact the usability of the system and lecturers’ presentation is satisfaction. The results showed, to produce a simple model called the “E-learning Success Model for Instructors’ Satisfactions” that is related to public, logical and technical communications of instructors in the entire e-learning system, the features associated with teachers’ satisfaction in e-learning systems have been examined.

The comparison between different E-learning tools in terms of their goals, benefits and drawbacks are presented in (Aljawarneh et al. 2010 ). The comparison assists in providing when to use each tool. The outcomes show that instructors and students prefer to use MOODLE over Blackboard in the e-learning environment. One of the major challenges that face the E-learning environment is security issues since security is not combined into the active learning development process.

The effect of e-learning at the Payame Noor University of Hamedan, Iran on the innovation and material awareness of chemistry students was examined in (Zare et al. 2016 ). The research used a control group's pre-test/post-test experimental design. Data analysis findings using the independent t-test showed significantly better scores on calculated variables, information and innovation for the experimental group. Consequently, E-learning is beneficial for the acquisition of knowledge and innovation among chemistry students, and that a larger chance for E-learning should be given for broader audiences.

A study in (Arkorful & Abaidoo, 2015 ) aimed to explore the literature and provide the study with a theoretical context by reviewing some publications made by different academics and universities on the definition of E-learning, its use in education and learning in institutions of higher education. The general literature described the pros and cons of E-learning, which showed that it needs to be enforced in higher education for teachers, supervisors and students to experience the full advantages of acceptance and implementation.

Assessing the learning effectiveness of e-learning was studied in (Somayeh et al. 2016 ). This analysis study was conducted using the databases of Medline and CINAHL and the search engine of Google. The research used covered review articles and English language meta-analysis. 38 papers including journals, books, and websites are investigated and categorized from the results obtained. The general advantages of E-learning such as the promotion of learning and speed and process of learning due to individual needs were discussed. The study results indicated positive effects of E-learning on learning, so it is proposed that more use should be made of this education method, which needs the requisite grounds to be established.

It is important to focus on analyzing the learner and student characteristics and motivating students to ensure their involvement in e-learning. Also, it is necessary to focus on the impact and extent of teacher acceptance of e-learning. The age difference between the teachers and the students indicates that the teachers received most of their studies and teaching skills through traditional teaching and learning methods, which may make their acceptance of e-learning different from the student’s acceptance of modern methods of e-learning and education in general.

The methodology

The descriptive-analytical method was used for this study and the five-point Likert-scale range was calculated based on (1) Strongly disagreed, (2) Disagree, (3) Neutral, (4) Agree, and (5) Strongly agree, with the analysis of results using a statistical application called the Statistical Package for the Social Sciences (SPSS).

Study population

The study targets the sample society that includes teaching staff and undergraduate students of all departments in the IT Faculty at the University of Benghazi.

Study boundaries

Scientific restrictions: Assessment of the extent of application of E-learning in higher education.

Administrative Field: Faculty of IT, University of Benghazi, Libya.

Period: The Year of 2020.

Human Resources: Teaching staff and students in the faculty.

Study sample

The study involves two types of questionnaires to be prepared and developed: one questionnaire for students and another for instructors. The following details were obtained after the questionnaires were randomly distributed and collected individually. The study sample was selected based on the awareness of the size of the population:

Student Questionnaire: The total number of distributed questionnaires was 140 copies, without invalid copies, and 5 copies were missing. Therefore, the copies being analyzed are 135.

Teaching Staff Questionnaire: The total number of distributed questionnaires was 20 copies, while 20 legitimate copies were returned without invalid or missing copies.

Some of the demographic characteristics are shown in Table 1 .

Study dimensions

The study has emphasized four dimensions to achieve the expected results as follows:

The extent of using E-learning in the Faculty of IT.

Advantages of E-learning.

Disadvantages of E-learning.

Obstacles to implementing E-learning.

Statistical analysis

Data analysis.

The Means and Materiality statistical relations are used to analyze the results. By evaluating the findings, we gain crucial information based on these statistical relations according to the rank of inquiries as shown in Tables 2 – 3 .

The students' perspective

The analysis of data as a statistical relationship regarding the perspective of the students is shown in Table 2 .

Dimension 1: the extent of using E-learning in IT faculty.

Inquiries (6), (7) and (10) are of similar materiality and inquiry (6) is chosen because it has the lower standard deviation, which states that "E-learning technologies are used for scientific research purposes" with the materiality of 82.6% and a mean 4.13, while inquiry number (7), which states "Search engines are used to obtain curriculum needs". However, inquiry (2), which states that "the Internet is available to students at the faculty” has the lowest materiality of 40% and a mean 2.

Dimension 2: advantages of E-learning

Inquiry number (1) states that "E-learning contributes to raising your educational level" has the highest materiality of 88.2% and a mean of 4.41. However, inquiry number (7), which states that "E-learning reduces the burden because learning becomes a conversation between teaching staff and students instead of traditional learning", has the lowest materiality of 75.8% and a mean of 3.79.

Dimension 3: disadvantages of E-learning

Inquiries (5) and (6) are of similar materiality and inquiry number (5) is chosen because it has the lower standard deviation, which states that "E-learning reduces the burden of teaching staff and increases the burden of students” with the materiality of 75.4% and a mean of 3.77. Nevertheless, inquiry number (1), which states that "E-learning isolates you from the community by connecting you to your computer for long periods ", was the lowest materiality of 72.6% and a mean of 3.63.

Dimension 4: obstacles to E-learning

Inquiry number (3) states that "the lack of the Internet in the faculty to apply E-learning" has the highest materiality of 79% and a mean of 3.95. Yet, inquiries (4) and (5) are of similar materiality and inquiry number (5) has been chosen as it has the lower standard deviation, which notes that "Lack of experience of students with E-learning techniques” with the materiality of 71.8% and a mean of 3.59.

Teaching staff perspective

The analysis of data as a statistical relationship regarding the perspective of the teaching staff and the important analyzes of mean and materiality is given in Table 3

Dimension 1: the extent of using E-learning in IT faculty

Inquiry number (10), which was about that “Use email to communicate with colleagues”, has the highest materiality of 91% and a mean of 4.55. However, inquiry number (2), which states that "internet accessible always available to teaching staff in the faculty", has the least materiality as 41.8% and the mean is 2.09.

Dimension 2. advantages of E-learning

Inquiry number (4) which states that "E-learning contributes to increasing students' skills in using computers” has the highest materiality of 84.6% and a mean of 4.23. However, inquiry number (7), which states that "E-learning reduces the burden because learning becomes a conversation between teaching staff and students instead of traditional learning” with the lowest materiality of 68.2% and a mean of 3.41.

Inquiry number (6) which states that "E-learning needs financial capability compared to traditional education" has the maximum materiality of 79% and a mean of 3.95. Nevertheless, inquiry number (3), which reports that "students face a greater burden during the educational process while reducing the burden of teaching staff", has the lowest materiality of 58.2% and a mean of 2.91.

Inquiries (4) and (7) are of similar materiality and inquiry number (4) is chosen because it has the lower standard deviation, which states that "The lack of internet in the faculty to apply e-learning" with the materiality of 82.8% and a mean 4.14. Yet, inquiries (3) and (6) are of similar materiality and inquiry (6) is chosen, which states that "E-learning needs high costs" has the lowest materiality of 71.8% and a mean of 3.59.

Results and discussion

Students' perspective.

As shown in Table 4 , we found the T-Test value = 8.733 and the P -Value = 0.00 to the extent of using E-learning during the pandemic. T-Test value = 22.86 and P -Value = 0.00 for the advantages of E-learning. The T-Test value = 12.786 and P -Value = 0.00 for the drawbacks of E-learning. The obstacles to E-learning in the last dimension are the T-Test value = 11.961 and the P -Value = 0.00. Accordingly, all T-Test values are greater than the T table value = 1.96. On the other side, all P -Values are smaller than the level of significance = 0.05. Thus, in each dimension of the four dimensions of the sample, there were statistically significant differences from the student's perspectives.

As shown in Table 5 , the extent, to which E-learning is used are T-Test = 6.021 and P -Value = 0.00, the advantages of E-learning are T-Test = 9.015 and P -Value = 0.00, the disadvantages of E-learning are T-Test = 3.813 and P -Value = 0.001, and the obstacles to E-learning are T-Test = 6.505 and P -Value = 0.00 respectively. Depending on the T-Test values are higher than the T table value = 1.96, P -Values are less than the level of significance = 0.05. There were statistically significant differences from the teaching staff perspective in each dimension of the study's four dimensions.

The data analysis of the four dimensions is summarized as follows:

The extent of the use of e-learning: the findings indicate that the student's approval of the use of e-learning and the teaching staff’s viewpoint is (Agreement), where the mean are (3.44) and (3.59) respectively.

The advantages of e-learning: the results consider this dimension indicates the approval of the advantages of e-learning from the perspective of students and teaching staff was (Agreement), where the mean of the perspective of students was (4.13) and the mean of the perspective of the teaching staff was (3.99).

The dimension that constituted the disadvantages of e-learning: This indicates that the student's acceptance drawbacks of e-learning are (Agreement) of the mean (3.78) and the teaching staff's opinion was (Undecided) of the mean (3.35).

The factor defining obstacles to e-learning indicates that there were acceptance obstacles for e-learning from the perspective of both students and teaching staff (i.e., Agreement), where the mean was (3.75) and (3.82).

A comparison between the two perspectives

As shown in Fig.  1 , it is noticeable that the viewpoint of both the teaching staff and the students in all four dimensions of the study is identical. This demonstrates that they are almost standardized, with little variation in the third dimension of the data considered for the disadvantages of e-learning during the Covid-19 pandemic. This factor achieves the agreement from the teaching staff's perspective and is undecided from the students' perspective to achieve the agreement as to the outcomes.

A comparison of students' and teaching staff' perspectives

The study outcomes

The study outcomes could be summaries as follows:

Findings based on students' perspective

The students believe that e-learning is used and that one of the most significant uses is a replica of the scientific method learned on electronic/multimedia forms.

The students agree that e-learning is useful and that it helps them to be safe and improved their academic standards.

The students claim that the introduction of e-learning is difficult and that the low-quality of internet services is the biggest obstacle to its application.

The students demonstrate that there are limitations to e-learning and that the biggest downside is that it decreases the workload for teaching staff and raises the pressure on students.

Findings based on teaching staff perspective

The teaching staff believes that e-learning is beneficial and that helping to develop students' technological skills is one of the most critical positive elements.

The teaching staff agrees that the use of e-learning is common and that the possession of faculty members via e-mail and other e-services is the most significant use.

The teaching staff agrees that there are barriers to the introduction of e-learning and that the high cost of its implementation is one of the main difficulties.

The teaching staff accepts that e-learning has disadvantages and that the biggest downside is that, relative to traditional learning, it requires financial support.

Pedagogical aspects

Any e-learning strategy follows one of the commonly known learning theories, i.e., behaviorism, cognitivism, or constructivism (Mödritscher, 2006 ). Furthermore, each didactic strategy has a more or less strong impact on the factors that influence the learning process and the self-assessment of the characteristics of the learner. Therefore, based on what has been achieved through the opinions of teaching staff and students, we found that the certain characteristics of the learner, in particular, the motivation need to be analyzed. It is also necessary, as an appropriate pedagogical step, to choose an e-learning strategy that suits the characteristics of students and the electronic environment they are living in nowadays.

Conclusion and recommendations

This study aims to identify the major issues and challenges by extrapolating the opinions of students and faculty instructors on the use of e-learning systems in a public university during the Covid-19 pandemic. The study society sample consists of students and faculty members at the Faculty of IT, University of Benghazi. The descriptive-analytical approach has been applied with statistical analysis of the results. Two types of questionnaires have been distributed for students and instructors. Four dimensions have been determined to reach the expected results, i.e., the extent to which e-learning is used and the advantages, disadvantages and obstacles to the implementation of E-learning in the Faculty of IT. Learning and teaching in an electronic environment still provide many advantages, including, reducing expenses and affords. It was also a successful alternative for many students to return to study in educational institutions during the spread of the Covid-19 virus, despite facing many issues and challenges. By analyzing the results, we have achieved encouraging results to highlight some of the issues, challenges and benefits of using the e-learning system in the higher education sector.

Issues such as technical and financial support, training, improved working conditions, technological background, skills, copyright protections and professional development are always important in the implementation of e-learning in public universities. Based on the study results, students believe that e-learning contributes to their learning. However, it reduces the workload on faculty and increases it on students. The main obstacle to e-learning is the low-quality of Internet services in Libya during the pandemic period. Faculty members agree that e-learning is useful in increasing students' computer skills, although it requires significant financial resources. We can claim that it is important to highlight many of the recommendations, which could have a positive impact on the possibility of implementing e-learning. The university has to provide internet service to students and teaching staff members with enough computer devices to apply e-learning. A modern electronic library and dedicated classrooms with all types of equipment and tools needed are also necessary to apply e-learning instead of coming to the main campus. Conducting online training and seminars regularly is important, for teaching staff, in particular, to support the application of e-learning, in addition to constant attention to IT infrastructure and periodic maintenance of computers and supporting equipment. In addition to all of this, the role and importance of focusing on many things related to the characteristics of the learner, such as the characteristics of the student's background knowledge and how to motivate the students as one of the pedagogical impacts.

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Maatuk, A.M., Elberkawi, E.K., Aljawarneh, S. et al. The COVID-19 pandemic and E-learning: challenges and opportunities from the perspective of students and instructors. J Comput High Educ 34 , 21–38 (2022). https://doi.org/10.1007/s12528-021-09274-2

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Research Article

The experiences, challenges, and acceptance of e-learning as a tool for teaching during the COVID-19 pandemic among university medical staff

Roles Conceptualization, Data curation, Methodology, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Community, Environmental and Occupational Medicine, Faculty of Medicine, Zagazig University, Zagazig, Egypt, Department of Family and Community Medicine, College of Medicine, Taibah University, Medina, KSA

Roles Conceptualization, Data curation, Resources, Writing – original draft

Affiliation Department of Community, Environmental and Occupational Medicine, Faculty of Medicine, Zagazig University, Zagazig, Egypt

Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

• Marwa Mohamed Zalat, 
• Mona Sami Hamed, 
• Sarah Abdelhalim Bolbol

• Published: March 26, 2021
• https://doi.org/10.1371/journal.pone.0248758
• Reader Comments

e-learning was underutilized in the past especially in developing countries. However, the current crisis of the COVID-19 pandemic forced the entire world to rely on it for education.

To estimate the university medical staff perceptions, evaluate their experiences, recognize their barriers, challenges of e-learning during the COVID-19 pandemic, and investigate factors influencing the acceptance and use of e-learning as a tool teaching within higher education.

Data was collected using an electronic questionnaire with a validated Technology Acceptance Model (TAM) for exploring factors that affect the acceptance and use of e-learning as a teaching tool among medical staff members, Zagazig University, Egypt.

The majority (88%) of the staff members agreed that the technological skills of giving the online courses increase the educational value of the experience of the college staff. The rate of participant agreement on perceived usefulness, perceived ease of use, and acceptance of e-learning was (77.1%, 76.5%, and 80.9% respectively). The highest barriers to e-learning were insufficient/ unstable internet connectivity (40%), inadequate computer labs (36%), lack of computers/ laptops (32%), and technical problems (32%). Younger age, teaching experience less than 10 years, and being a male are the most important indicators affecting e-learning acceptance.

This study highlights the challenges and factors influencing the acceptance, and use of e-learning as a tool for teaching within higher education. Thus, it will help to develop a strategic plan for the successful implementation of e-learning and view technology as a positive step towards evolution and change.

Citation: Zalat MM, Hamed MS, Bolbol SA (2021) The experiences, challenges, and acceptance of e-learning as a tool for teaching during the COVID-19 pandemic among university medical staff. PLoS ONE 16(3): e0248758. https://doi.org/10.1371/journal.pone.0248758

Editor: Gwo-Jen Hwang, National Taiwan University of Science and Technology, TAIWAN

Received: November 11, 2020; Accepted: March 4, 2021; Published: March 26, 2021

Copyright: © 2021 Zalat et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: within the manuscript.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

COVID-19, a public health crisis of worldwide importance, was announced by the World Health Organization (WHO) in January 2020 as a new coronavirus disease outbreak and was reported as a pandemic in March 2020 [ 1 ].

Egypt reported the first German tourist death due to the virus on March 8. The increase in the number of cases to more than 100 cases by mid-March forced the government to make more rigid regulations. For one month, Egypt closed schools and universities and facilitated online distance electronic learning (e-learning) [ 2 ].

The pandemic of COVID-19 caused several schools and colleges to remain temporarily closed. Face-to-face education has ended by numerous schools, universities, and colleges. This will have negative impacts on educational activities, as social distance is crucial at this stage. Educational agencies are trying to find alternatives ways to manage this difficult circumstance [ 3 ]. This shutdown stimulated the growth of online educational activities so that there would be no interruption to education. Many faculties have been involved in how best to offer online course material, involve students, and perform evaluations [ 4 ].

This crisis would make the new technology accepted by organizations that were previously resistant to adapt. This was a difficult time for the educational sectors to deal with the current situation; professional education, particularly medical education, was more challenging [ 5 ].

Online e-learning is described as learning experiences using various electronic devices (e.g. computers, laptops, smartphones, etc.) with internet availability in synchronous or asynchronous environmental conditions. Online e-learning could be a platform that makes the process of education more student-centered, creative, and flexible [ 6 ]. Online delivery of courses is cost-effective and easily accessible especially when delivering curriculum to students in rural and remote areas [ 3 ]. The United online e-learning is seen by the United Nations (UN) and the WHO as a helpful tool for meeting educational needs, especially in developing nations [ 7 ]. Medical colleges have implemented numerous creative strategies to combat the crisis, using various software/apps such as Google Classroom, Zoom, and Microsoft Teams to take online courses. In order not only to complete the course but also to stay in constant contact with the learners, this virtual class of e-learning was initiated to grow the certainty and confidence of the students in their faculty during the COVID-19 pandemic [ 5 ].

It is anticipated that with the implementation of e-learning, the role of faculty members will be transformed from the traditional teacher-centric to student-centric model which serves the current new curriculum applied at our college of medicine. Therefore, this study aims to estimate the university staff perceptions, evaluate their experiences, recognize their barriers, and assess their challenges to e-learning during the COVID-19 pandemic. Additionally, the study will investigate factors influencing the acceptance of e-learning as a tool for teaching within higher education which could help future endeavors aimed at implementing e-learning not only during the pandemic but in other non-pandemic situations throughout the teaching life.

Materials and methods

Study design and setting.

A cross-sectional study was conducted from September 1st to October 1st, 2020 at the Faculty of Medicine, Zagazig University, Sharkia Governorate, Egypt.

Study population and sample size

The medical staff of both basic science and clinical departments who are engaged in the development and teaching of online courses were invited to participate in the study. While, those who refused participation, retired, or on leaves (e.g. sick, maternity, or any type of leaves) were excluded.

The required sample size was calculated to be 346 staff members. Calculations have been done using the sample size software online for prevalence studies [ 8 ]: the total number of staff members in both basic science departments (i.e. anatomy, physiology, pathology, histology, biochemistry, parasitology, pharmacology, microbiology), and clinical departments (i.e. internal medicine, surgery, gynecology & obstetric, pediatrics, community medicine, family medicine …..etc.) was 3439 at the faculty of Medicine, Zagazig university at the time of the study, assuming a prevalence of 50%, a precision of 5% at confidence interval 95% and power of test 80%.

Tools of data collection

A semi-tailored electronic questionnaire was used and contains four parts:

First Part : questions on socio-demographic and occupational data of the participants as age, gender, marital status, residence, work sector (academic or clinical), current employment status, years of teaching experience, whether they have taught an online course before or not, and their experience duration.

Second part : questions on university staff perceptions and experiences of online courses adapted from a previous study [ 9 ]. The questions are rated on a 5-point scale ranging from strongly disagree = 1 to strongly agree = 5 by which the staff member could express their agreement levels.

Third Part : questions on barriers and challenges towards online learning. Medical staff should rank the challenges facing distance education in order of their seriousness (1–10 scale, 1 being the least serious, 10 being the most serious) [ 10 ].

Fourth part: questions based on the validated Technology Acceptance Model (TAM) [ 11 ], for exploring factors that affect university medical staff acceptance and use of e-learning as a teaching tool. It consisted of three items namely perceived usefulness, perceived ease of use, and acceptance on a 5-point scale ranging from ‘‘strongly disagree” to ‘‘strongly agree.”, Acceptance was categorized as accept and don’t accept according to the median (median = 2.5), scores above 2.5 indicate acceptance while rated scores <2.5 indicate refusal.

Data analysis techniques used for detection of the percentage of respondents’ response is described in detail in the work of Napitupulu et al. [ 12 ] and the range of results compared to the following categories: 0–25% Strongly Disagree, 26–50% Disagree, 51–75% Agree, 76–100% Strongly Agree.

Procedures of data collection

The electronic questionnaire was designed on Google forms, and the invitation link for participation in the survey was shared via mail and on social media such as each department WhatsApp group, by the researchers, through the departments’ coordinators. Another two reminders were sent every 10 days to increase the participants’ response rate. A cover letter was presented on the first page of each electronic survey explaining the purpose of the study, emphasizing its importance and significance, therefore encouraging cooperation by the respondents.

Pilot study

The questionnaire was tested on 10 staff members. The necessary modifications, changes, and corrections were done to ensure ease of understanding and clarification of all questions. For testing the questionnaire reliability, Cronbach’s alpha test was used and was >0.70 for most of the items.

Data management

Data were analyzed using the SPSS version 20.0. The Shapiro-Wilk test was used to assess the normality of data distribution. Descriptive analysis was performed for quantitative data by mean, standard deviations and for qualitative data by frequencies and percentages as applicable. A Multivariate regression analysis was performed to predict potentially significant determinants of acceptance and use of e-learning in education. A P-value of < 0.05 was considered statistically significant.

Ethical considerations

The necessary official permissions were obtained from the Zagazig University Institutional Review Board (Ref No #6385-1-9-2020#). Consent from the participant after being informed about the purpose of the study and research objectives was obtained at the start of the online survey. Privacy and confidentiality were assured.

A total participant in this study was 346 university medical staff members. Most of the participants are females (87.9%) with a mean age of 47 years most of them are married (72%). Most of the staff members live in the same city where they work (76%) with a mean of 19 years of teaching experience, and more than half of them (63.9%) were from the basic science departments. Half of the teaching staff are professors (52%) and taught online courses before (40.2%) for more than 2 years and taught both theoretical and practical sessions ( Table 1 ).

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https://doi.org/10.1371/journal.pone.0248758.t001

Study results revealed that all the staff members agreed that the online course design permits staff to educate at their own speed (36.1% strongly agreed and 63.9% agreed), followed by 88% of the staff members agreed that the technological skills acquired from teaching online courses increased their educational experience (56.1% strongly agreed and 32.1% agreed). While 44.2% of staff members disagreed that tests in an online course are more difficult for students (4% strongly disagreed and 40.2% disagreed) compared to 43.9% agreement (7.8% agree and 36.1% strongly agree) ( Table 2 ).

https://doi.org/10.1371/journal.pone.0248758.t002

Applying the Technology Acceptance Model (TAM) to university medical staff members showed that the percentage of the respondent’s answer on perceived usefulness was 77.1%, this means that university medical staff found that e-learning is very helpful in improving and progressing the educational process. The percentage of the respondent’s answer on perceived ease of use was 76.5%, this means that users assess e-learning systems implemented by being highly easy to use and operate. While the percentage of the respondent’s answer on acceptance of e-learning was 80.9%, this means that based on user perception, the e-learning system implemented was with high acceptance level. This was obtained because perceived ease of use and perceived usefulness have been assessed to be adequate for the users ( Table 3 ).

https://doi.org/10.1371/journal.pone.0248758.t003

Studying the barriers of e-learning as reported by the university staff members showed that (40%) reported insufficient/ unstable internet connectivity followed by inadequate computer labs (36%), lack of computers/ laptops (32%), and technical problems (32%) ( Table 4 ).

https://doi.org/10.1371/journal.pone.0248758.t004

Statistical analysis was conducted to identify risk factors in terms of unadjusted OR. Teaching experience duration (years) followed by the online courses they taught before COVID-19, age of staff members (years), and work sector whether academic or clinical were the significant factors that influence acceptance of e-learning. A logistic regression analysis was done to study the significant independent factors affecting e-learning acceptance and showed that age under 40 years, teaching experience less than 10 years, and being a male are the most important indicators affecting e-learning acceptance ( Table 5 ).

https://doi.org/10.1371/journal.pone.0248758.t005

e-learning is not considered a new phenomenon, there was an increasing global trend of using electronic learning or e-learning in the last decade and some higher education institutes in developing countries have adopted this trend recently [ 13 ]. However, this technology has not been evenly dispersed throughout all nations and cultures [ 14 ].

More than nine months have passed since the WHO declaration of COVID-19 as a pandemic, with an abrupt shift to online teaching and electronic learning. Furthermore, the uncertain future concerning returning to normal life and stopping this pandemic results in maximum dependency on e-learning especially in higher education [ 15 ].

Like other countries, Egypt faced significant challenges in higher education and transferred its in-person educational system to virtual learning. A particular urgent challenge was for face-to-face university courses to be delivered online [ 16 ]. In this study, the e-learning perception, challenges, and predictors of its acceptance as a method for education during the COVID-19 pandemic were investigated among the university medical staff members.

The majority of the participants agreed (32.1%) and strongly agreed (56.1%) that the technological skills to provide online courses increase the educational value of the experience of the faculty staff members. Similarly, these findings from our research support the results of previous studies [ 17 – 19 ].

The majority of our participants agreed (59.5%) on the advantages of time flexibility of teaching the online course. In contrast, other previous studies [ 19 ], reported that faculty members considered that e-learning can take time and can lead to student monitoring difficulties and can reduce the interest in direct traditional teaching.

These various perceptions might be related to unfamiliarity with the e-learning medium, different technological knowledge, and skills of the participants which highlight the need for formal training and workshops on using various technological methods and platforms for strengthening the e-learning activities.

The current study showed that 36.1% and 63.9% of the participants strongly agreed, and agreed respectively that the online course enables staff to teach at their own pace. Similarly, a previous study appreciated the self-pacing of online learning [ 20 ].

Also, most of our participants disagreed/ strongly disagreed (44.2%) that exams in an online course are harder for students. The reason for this staff perception might be attributed to the fact that most of the online tests are based on multiple-choice questions which allow testing a large number of students quickly, and across a vast expanse of content than essay questions. Furthermore, the automated marking of the tests saves the staff members efforts and time [ 21 ]. On the contrary, another study by Hannafin et al. [ 22 ] noted that many observational and participatory evaluations of distant learning were difficult. Likewise, Oncu & Cakir [ 23 ] noticed that because of the lack of face-to-face interaction, informal assessment can be challenging for online instructors. Nevertheless, there are indeed best practices and techniques for conducting assessments securely with a sort of protection system in the online environment.

In the present study, the application of the TAM on our participants revealed that a higher percentage of the respondents agreed with the perceived usefulness of e-learning which means that university medical staff accepts that e-learning is valuable in improving and progressing the teaching and learning process. Meanwhile, prior research by Poon et al. [ 24 ] reported that their participants at several local universities were not fully comfortable with e-learning as a tool for teaching and attributed this perception to many factors as technological challenges, difficult interactions and discussions with students, lack of adequate internet connectivity and personal learning preference [ 25 ].

Inconsistent with Choreki [ 26 ], our survey findings bring to light that most of the respondents agreed on the ease of use of e-learning which means that medical staff assesses e-learning systems implemented by being profoundly simple to use and operate. This could be attributed to the fact that our college was recently started their new blended learning program (i.e. the combination of e-learning technology with the traditional face-to-face teaching) short times before the COVID-19 pandemic with intensive training for all staff members on the online courses, planning and designing the teaching materials before its formal application for students.

In our college, both synchronous (live or in real-time) and asynchronous (recorded or self-paced) e-learning strategies were implemented through learning management systems (LMS) with their applications (e.g. Zoom and Microsoft Teams). Synchronous e-learning was offered in the form of interactive teaching and clinical case discussions in small and large group formats. Asynchronous e-learning included preparation of course materials for students in advance of students’ access (e.g. recorded lectures, supportive videos, external links for recommended websites, and additional resources such as electronic books). These enhance the staff adoption of the new technology and its integration into their teaching activities [ 19 ].

This study showed that the e-learning system was implemented with a high acceptance level. Several studies were done in different countries [ 27 – 29 ] reported that the user adoption and acceptance of e-learning were influenced by a diverse individual (e.g. readiness to use e-learning), social (e.g. interpersonal and instructor influence), and organizational (e.g. technological facilities, financial and infrastructure) factors within a specific culture, in addition to the perceived benefit and ease of use of e-learning systems.

Studying the barriers of e-learning as reported by our survey revealed that reported insufficient/ unstable internet connectivity, inadequate computer labs, lack of computers/ laptops, and technical problems were the highest challenge for adapting to e-learning. In alignment with these findings, recent research by Nguyen et al. [ 30 ] demonstrated that the main obstacles to e-learning are based on several stakeholder perspectives of infrastructure, technology, management, support, execution, and pedagogical aspects. Likewise, another study illustrated that e-learning tools should meet the users’ requirements to gain their trust and improve their acceptance of e-learning [ 31 ]. Additional study classified e-learning barriers into learners, teachers, curriculum, organizational and structural factors that need more collaboration for their solutions [ 32 ].

As regards the factors predicting the acceptance of e-learning, the logistic regression analysis showed that age under 40 years, teaching experience less than 10 years, and male gender are the most important indicators affecting e-learning acceptance. This could be clarified by the reality that younger staff already using technology in general than older, which would increase their abilities, willingness, and acceptance to use other e-learning technology. Furthermore, this result is in agreement with Fischer et al. [ 33 ] who stated that older staff with long traditional teaching experience usually has limited interaction with technology and lacking the development of their necessary skills.

Adamus et al. [ 34 ], reported women’s preference for accepting e-learning than men’s. In contrast, past studies showed unfavorable differences for women due to mental overload, stress, and difficulties with work-life balance [ 35 , 36 ].

Meanwhile, other studies reported scarce differences between males and females in their use of e-learning, their motivation, and satisfaction [ 37 ]. The reason for this difference may be related to different gender representation in the studies.

Limitation of the study

This study has some potential limitations. Being a cross-sectional study, the participants’ perceptions may change over time. Therefore, a further longitudinal study is required to enhance the understanding of determinants that are critical to the adoption of e-learning systems in our community. Also, the present study was conducted in one medical college. So, in the future, additional studies need to be done using subjects from other universities to assess the adoption and acceptance of e-learning in higher educational institutes.

Conclusions

e-learning was underutilized in the past, especially in developing countries. However, the current crisis of the COVID-19 pandemic enforced the entire world to rely on it for education.

In the current study, the majority of participants strongly agreed with the perceived usefulness, perceived ease of use, and acceptance of e-learning. The highest challenge for accepting e-learning were insufficient/ unstable internet connectivity, inadequate computer labs, lack of computers/ laptops, and technical problems. The significant indicators affecting e-learning acceptance were age under 40 years, teaching experience less than 10 years, and male gender. This study highlights the challenges and factors affecting the acceptance of e-learning as a tool for teaching within higher education, in developing countries and may lead to strategic development and implementation of e-learning and view technology as a positive step towards evolution and change.

Supporting information

S1 dataset..

https://doi.org/10.1371/journal.pone.0248758.s001

Acknowledgments

We would like to acknowledge all the medical staff members who participated in and contributed samples to the study for their cooperation and help in facilitating data collection.

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The COVID-19 pandemic has changed education forever. This is how 

With schools shut across the world, millions of children have had to adapt to new types of learning. Image:  REUTERS/Gonzalo Fuentes

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Farah lalani.

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.chakra .wef-1nk5u5d{margin-top:16px;margin-bottom:16px;line-height:1.388;color:#2846F8;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-1nk5u5d{font-size:1.125rem;}} Get involved with our crowdsourced digital platform to deliver impact at scale

Stay up to date:.

• The COVID-19 has resulted in schools shut all across the world. Globally, over 1.2 billion children are out of the classroom.
• As a result, education has changed dramatically, with the distinctive rise of e-learning, whereby teaching is undertaken remotely and on digital platforms.
• Research suggests that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay.

While countries are at different points in their COVID-19 infection rates, worldwide there are currently more than 1.2 billion children in 186 countries affected by school closures due to the pandemic. In Denmark, children up to the age of 11 are returning to nurseries and schools after initially closing on 12 March , but in South Korea students are responding to roll calls from their teachers online .

With this sudden shift away from the classroom in many parts of the globe, some are wondering whether the adoption of online learning will continue to persist post-pandemic, and how such a shift would impact the worldwide education market.

Even before COVID-19, there was already high growth and adoption in education technology, with global edtech investments reaching US$18.66 billion in 2019 and the overall market for online education projected to reach $350 Billion by 2025 . Whether it is language apps , virtual tutoring , video conferencing tools, or online learning software , there has been a significant surge in usage since COVID-19.

How is the education sector responding to COVID-19?

In response to significant demand, many online learning platforms are offering free access to their services, including platforms like BYJU’S , a Bangalore-based educational technology and online tutoring firm founded in 2011, which is now the world’s most highly valued edtech company . Since announcing free live classes on its Think and Learn app, BYJU’s has seen a 200% increase in the number of new students using its product, according to Mrinal Mohit, the company's Chief Operating Officer.

Tencent classroom, meanwhile, has been used extensively since mid-February after the Chinese government instructed a quarter of a billion full-time students to resume their studies through online platforms. This resulted in the largest “online movement” in the history of education with approximately 730,000 , or 81% of K-12 students, attending classes via the Tencent K-12 Online School in Wuhan.

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Other companies are bolstering capabilities to provide a one-stop shop for teachers and students. For example, Lark, a Singapore-based collaboration suite initially developed by ByteDance as an internal tool to meet its own exponential growth, began offering teachers and students unlimited video conferencing time, auto-translation capabilities, real-time co-editing of project work, and smart calendar scheduling, amongst other features. To do so quickly and in a time of crisis, Lark ramped up its global server infrastructure and engineering capabilities to ensure reliable connectivity.

Alibaba’s distance learning solution, DingTalk, had to prepare for a similar influx: “To support large-scale remote work, the platform tapped Alibaba Cloud to deploy more than 100,000 new cloud servers in just two hours last month – setting a new record for rapid capacity expansion,” according to DingTalk CEO, Chen Hang.

Some school districts are forming unique partnerships, like the one between The Los Angeles Unified School District and PBS SoCal/KCET to offer local educational broadcasts, with separate channels focused on different ages, and a range of digital options. Media organizations such as the BBC are also powering virtual learning; Bitesize Daily , launched on 20 April, is offering 14 weeks of curriculum-based learning for kids across the UK with celebrities like Manchester City footballer Sergio Aguero teaching some of the content.

What does this mean for the future of learning?

While some believe that the unplanned and rapid move to online learning – with no training, insufficient bandwidth, and little preparation – will result in a poor user experience that is unconducive to sustained growth, others believe that a new hybrid model of education will emerge, with significant benefits. “I believe that the integration of information technology in education will be further accelerated and that online education will eventually become an integral component of school education,“ says Wang Tao, Vice President of Tencent Cloud and Vice President of Tencent Education.

There have already been successful transitions amongst many universities. For example, Zhejiang University managed to get more than 5,000 courses online just two weeks into the transition using “DingTalk ZJU”. The Imperial College London started offering a course on the science of coronavirus, which is now the most enrolled class launched in 2020 on Coursera .

Many are already touting the benefits: Dr Amjad, a Professor at The University of Jordan who has been using Lark to teach his students says, “It has changed the way of teaching. It enables me to reach out to my students more efficiently and effectively through chat groups, video meetings, voting and also document sharing, especially during this pandemic. My students also find it is easier to communicate on Lark. I will stick to Lark even after coronavirus, I believe traditional offline learning and e-learning can go hand by hand."

These 3 charts show the global growth in online learning

The challenges of online learning.

There are, however, challenges to overcome. Some students without reliable internet access and/or technology struggle to participate in digital learning; this gap is seen across countries and between income brackets within countries. For example, whilst 95% of students in Switzerland, Norway, and Austria have a computer to use for their schoolwork, only 34% in Indonesia do, according to OECD data .

In the US, there is a significant gap between those from privileged and disadvantaged backgrounds: whilst virtually all 15-year-olds from a privileged background said they had a computer to work on, nearly 25% of those from disadvantaged backgrounds did not. While some schools and governments have been providing digital equipment to students in need, such as in New South Wales , Australia, many are still concerned that the pandemic will widenthe digital divide .

Is learning online as effective?

For those who do have access to the right technology, there is evidence that learning online can be more effective in a number of ways. Some research shows that on average, students retain 25-60% more material when learning online compared to only 8-10% in a classroom. This is mostly due to the students being able to learn faster online; e-learning requires 40-60% less time to learn than in a traditional classroom setting because students can learn at their own pace, going back and re-reading, skipping, or accelerating through concepts as they choose.

Nevertheless, the effectiveness of online learning varies amongst age groups. The general consensus on children, especially younger ones, is that a structured environment is required , because kids are more easily distracted. To get the full benefit of online learning, there needs to be a concerted effort to provide this structure and go beyond replicating a physical class/lecture through video capabilities, instead, using a range of collaboration tools and engagement methods that promote “inclusion, personalization and intelligence”, according to Dowson Tong, Senior Executive Vice President of Tencent and President of its Cloud and Smart Industries Group.

Since studies have shown that children extensively use their senses to learn, making learning fun and effective through use of technology is crucial, according to BYJU's Mrinal Mohit. “Over a period, we have observed that clever integration of games has demonstrated higher engagement and increased motivation towards learning especially among younger students, making them truly fall in love with learning”, he says.

A changing education imperative

It is clear that this pandemic has utterly disrupted an education system that many assert was already losing its relevance . In his book, 21 Lessons for the 21st Century , scholar Yuval Noah Harari outlines how schools continue to focus on traditional academic skills and rote learning , rather than on skills such as critical thinking and adaptability, which will be more important for success in the future. Could the move to online learning be the catalyst to create a new, more effective method of educating students? While some worry that the hasty nature of the transition online may have hindered this goal, others plan to make e-learning part of their ‘new normal’ after experiencing the benefits first-hand.

The importance of disseminating knowledge is highlighted through COVID-19

Major world events are often an inflection point for rapid innovation – a clear example is the rise of e-commerce post-SARS . While we have yet to see whether this will apply to e-learning post-COVID-19, it is one of the few sectors where investment has not dried up . What has been made clear through this pandemic is the importance of disseminating knowledge across borders, companies, and all parts of society. If online learning technology can play a role here, it is incumbent upon all of us to explore its full potential.

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Online and face‐to‐face learning: Evidence from students’ performance during the Covid‐19 pandemic

Carolyn chisadza.

1 Department of Economics, University of Pretoria, Hatfield South Africa

Matthew Clance

Thulani mthembu.

2 Department of Education Innovation, University of Pretoria, Hatfield South Africa

Nicky Nicholls

Eleni yitbarek.

This study investigates the factors that predict students' performance after transitioning from face‐to‐face to online learning as a result of the Covid‐19 pandemic. It uses students' responses from survey questions and the difference in the average assessment grades between pre‐lockdown and post‐lockdown at a South African university. We find that students' performance was positively associated with good wifi access, relative to using mobile internet data. We also observe lower academic performance for students who found transitioning to online difficult and who expressed a preference for self‐study (i.e. reading through class slides and notes) over assisted study (i.e. joining live lectures or watching recorded lectures). The findings suggest that improving digital infrastructure and reducing the cost of internet access may be necessary for mitigating the impact of the Covid‐19 pandemic on education outcomes.

1. INTRODUCTION

The Covid‐19 pandemic has been a wake‐up call to many countries regarding their capacity to cater for mass online education. This situation has been further complicated in developing countries, such as South Africa, who lack the digital infrastructure for the majority of the population. The extended lockdown in South Africa saw most of the universities with mainly in‐person teaching scrambling to source hardware (e.g. laptops, internet access), software (e.g. Microsoft packages, data analysis packages) and internet data for disadvantaged students in order for the semester to recommence. Not only has the pandemic revealed the already stark inequality within the tertiary student population, but it has also revealed that high internet data costs in South Africa may perpetuate this inequality, making online education relatively inaccessible for disadvantaged students. 1

The lockdown in South Africa made it possible to investigate the changes in second‐year students' performance in the Economics department at the University of Pretoria. In particular, we are interested in assessing what factors predict changes in students' performance after transitioning from face‐to‐face (F2F) to online learning. Our main objectives in answering this study question are to establish what study materials the students were able to access (i.e. slides, recordings, or live sessions) and how students got access to these materials (i.e. the infrastructure they used).

The benefits of education on economic development are well established in the literature (Gyimah‐Brempong,  2011 ), ranging from health awareness (Glick et al.,  2009 ), improved technological innovations, to increased capacity development and employment opportunities for the youth (Anyanwu,  2013 ; Emediegwu,  2021 ). One of the ways in which inequality is perpetuated in South Africa, and Africa as a whole, is through access to education (Anyanwu,  2016 ; Coetzee,  2014 ; Tchamyou et al.,  2019 ); therefore, understanding the obstacles that students face in transitioning to online learning can be helpful in ensuring more equal access to education.

Using students' responses from survey questions and the difference in the average grades between pre‐lockdown and post‐lockdown, our findings indicate that students' performance in the online setting was positively associated with better internet access. Accessing assisted study material, such as narrated slides or recordings of the online lectures, also helped students. We also find lower academic performance for students who reported finding transitioning to online difficult and for those who expressed a preference for self‐study (i.e. reading through class slides and notes) over assisted study (i.e. joining live lectures or watching recorded lectures). The average grades between pre‐lockdown and post‐lockdown were about two points and three points lower for those who reported transitioning to online teaching difficult and for those who indicated a preference for self‐study, respectively. The findings suggest that improving the quality of internet infrastructure and providing assisted learning can be beneficial in reducing the adverse effects of the Covid‐19 pandemic on learning outcomes.

Our study contributes to the literature by examining the changes in the online (post‐lockdown) performance of students and their F2F (pre‐lockdown) performance. This approach differs from previous studies that, in most cases, use between‐subject designs where one group of students following online learning is compared to a different group of students attending F2F lectures (Almatra et al.,  2015 ; Brown & Liedholm,  2002 ). This approach has a limitation in that that there may be unobserved characteristics unique to students choosing online learning that differ from those choosing F2F lectures. Our approach avoids this issue because we use a within‐subject design: we compare the performance of the same students who followed F2F learning Before lockdown and moved to online learning during lockdown due to the Covid‐19 pandemic. Moreover, the study contributes to the limited literature that compares F2F and online learning in developing countries.

Several studies that have also compared the effectiveness of online learning and F2F classes encounter methodological weaknesses, such as small samples, not controlling for demographic characteristics, and substantial differences in course materials and assessments between online and F2F contexts. To address these shortcomings, our study is based on a relatively large sample of students and includes demographic characteristics such as age, gender and perceived family income classification. The lecturer and course materials also remained similar in the online and F2F contexts. A significant proportion of our students indicated that they never had online learning experience before. Less than 20% of the students in the sample had previous experience with online learning. This highlights the fact that online education is still relatively new to most students in our sample.

Given the global experience of the fourth industrial revolution (4IR), 2 with rapidly accelerating technological progress, South Africa needs to be prepared for the possibility of online learning becoming the new norm in the education system. To this end, policymakers may consider engaging with various organizations (schools, universities, colleges, private sector, and research facilities) To adopt interventions that may facilitate the transition to online learning, while at the same time ensuring fair access to education for all students across different income levels. 3

1.1. Related literature

Online learning is a form of distance education which mainly involves internet‐based education where courses are offered synchronously (i.e. live sessions online) and/or asynchronously (i.e. students access course materials online in their own time, which is associated with the more traditional distance education). On the other hand, traditional F2F learning is real time or synchronous learning. In a physical classroom, instructors engage with the students in real time, while in the online format instructors can offer real time lectures through learning management systems (e.g. Blackboard Collaborate), or record the lectures for the students to watch later. Purely online courses are offered entirely over the internet, while blended learning combines traditional F2F classes with learning over the internet, and learning supported by other technologies (Nguyen,  2015 ).

Moreover, designing online courses requires several considerations. For example, the quality of the learning environment, the ease of using the learning platform, the learning outcomes to be achieved, instructor support to assist and motivate students to engage with the course material, peer interaction, class participation, type of assessments (Paechter & Maier,  2010 ), not to mention training of the instructor in adopting and introducing new teaching methods online (Lundberg et al.,  2008 ). In online learning, instructors are more facilitators of learning. On the other hand, traditional F2F classes are structured in such a way that the instructor delivers knowledge, is better able to gauge understanding and interest of students, can engage in class activities, and can provide immediate feedback on clarifying questions during the class. Additionally, the designing of traditional F2F courses can be less time consuming for instructors compared to online courses (Navarro,  2000 ).

Online learning is also particularly suited for nontraditional students who require flexibility due to work or family commitments that are not usually associated with the undergraduate student population (Arias et al.,  2018 ). Initially the nontraditional student belonged to the older adult age group, but with blended learning becoming more commonplace in high schools, colleges and universities, online learning has begun to traverse a wider range of age groups. However, traditional F2F classes are still more beneficial for learners that are not so self‐sufficient and lack discipline in working through the class material in the required time frame (Arias et al.,  2018 ).

For the purpose of this literature review, both pure online and blended learning are considered to be online learning because much of the evidence in the literature compares these two types against the traditional F2F learning. The debate in the literature surrounding online learning versus F2F teaching continues to be a contentious one. A review of the literature reveals mixed findings when comparing the efficacy of online learning on student performance in relation to the traditional F2F medium of instruction (Lundberg et al.,  2008 ; Nguyen,  2015 ). A number of studies conducted Before the 2000s find what is known today in the empirical literature as the “No Significant Difference” phenomenon (Russell & International Distance Education Certificate Center (IDECC),  1999 ). The seminal work from Russell and IDECC ( 1999 ) involved over 350 comparative studies on online/distance learning versus F2F learning, dating back to 1928. The author finds no significant difference overall between online and traditional F2F classroom education outcomes. Subsequent studies that followed find similar “no significant difference” outcomes (Arbaugh,  2000 ; Fallah & Ubell,  2000 ; Freeman & Capper,  1999 ; Johnson et al.,  2000 ; Neuhauser,  2002 ). While Bernard et al. ( 2004 ) also find that overall there is no significant difference in achievement between online education and F2F education, the study does find significant heterogeneity in student performance for different activities. The findings show that students in F2F classes outperform the students participating in synchronous online classes (i.e. classes that require online students to participate in live sessions at specific times). However, asynchronous online classes (i.e. students access class materials at their own time online) outperform F2F classes.

More recent studies find significant results for online learning outcomes in relation to F2F outcomes. On the one hand, Shachar and Yoram ( 2003 ) and Shachar and Neumann ( 2010 ) conduct a meta‐analysis of studies from 1990 to 2009 and find that in 70% of the cases, students taking courses by online education outperformed students in traditionally instructed courses (i.e. F2F lectures). In addition, Navarro and Shoemaker ( 2000 ) observe that learning outcomes for online learners are as effective as or better than outcomes for F2F learners, regardless of background characteristics. In a study on computer science students, Dutton et al. ( 2002 ) find online students perform significantly better compared to the students who take the same course on campus. A meta‐analysis conducted by the US Department of Education finds that students who took all or part of their course online performed better, on average, than those taking the same course through traditional F2F instructions. The report also finds that the effect sizes are larger for studies in which the online learning was collaborative or instructor‐driven than in those studies where online learners worked independently (Means et al.,  2010 ).

On the other hand, evidence by Brown and Liedholm ( 2002 ) based on test scores from macroeconomics students in the United States suggest that F2F students tend to outperform online students. These findings are supported by Coates et al. ( 2004 ) who base their study on macroeconomics students in the United States, and Xu and Jaggars ( 2014 ) who find negative effects for online students using a data set of about 500,000 courses taken by over 40,000 students in Washington. Furthermore, Almatra et al. ( 2015 ) compare overall course grades between online and F2F students for a Telecommunications course and find that F2F students significantly outperform online learning students. In an experimental study where students are randomly assigned to attend live lectures versus watching the same lectures online, Figlio et al. ( 2013 ) observe some evidence that the traditional format has a positive effect compared to online format. Interestingly, Callister and Love ( 2016 ) specifically compare the learning outcomes of online versus F2F skills‐based courses and find that F2F learners earned better outcomes than online learners even when using the same technology. This study highlights that some of the inconsistencies that we find in the results comparing online to F2F learning might be influenced by the nature of the course: theory‐based courses might be less impacted by in‐person interaction than skills‐based courses.

The fact that the reviewed studies on the effects of F2F versus online learning on student performance have been mainly focused in developed countries indicates the dearth of similar studies being conducted in developing countries. This gap in the literature may also highlight a salient point: online learning is still relatively underexplored in developing countries. The lockdown in South Africa therefore provides us with an opportunity to contribute to the existing literature from a developing country context.

2. CONTEXT OF STUDY

South Africa went into national lockdown in March 2020 due to the Covid‐19 pandemic. Like most universities in the country, the first semester for undergraduate courses at the University of Pretoria had already been running since the start of the academic year in February. Before the pandemic, a number of F2F lectures and assessments had already been conducted in most courses. The nationwide lockdown forced the university, which was mainly in‐person teaching, to move to full online learning for the remainder of the semester. This forced shift from F2F teaching to online learning allows us to investigate the changes in students' performance.

Before lockdown, classes were conducted on campus. During lockdown, these live classes were moved to an online platform, Blackboard Collaborate, which could be accessed by all registered students on the university intranet (“ClickUP”). However, these live online lectures involve substantial internet data costs for students. To ensure access to course content for those students who were unable to attend the live online lectures due to poor internet connections or internet data costs, several options for accessing course content were made available. These options included prerecorded narrated slides (which required less usage of internet data), recordings of the live online lectures, PowerPoint slides with explanatory notes and standard PDF lecture slides.

At the same time, the university managed to procure and loan out laptops to a number of disadvantaged students, and negotiated with major mobile internet data providers in the country for students to have free access to study material through the university's “connect” website (also referred to as the zero‐rated website). However, this free access excluded some video content and live online lectures (see Table  1 ). The university also provided between 10 and 20 gigabytes of mobile internet data per month, depending on the network provider, sent to students' mobile phones to assist with internet data costs.

Sites available on zero‐rated website

Note : The table summarizes the sites that were available on the zero‐rated website and those that incurred data costs.

High data costs continue to be a contentious issue in Africa where average incomes are low. Gilbert ( 2019 ) reports that South Africa ranked 16th of the 45 countries researched in terms of the most expensive internet data in Africa, at US$6.81 per gigabyte, in comparison to other Southern African countries such as Mozambique (US$1.97), Zambia (US$2.70), and Lesotho (US$4.09). Internet data prices have also been called into question in South Africa after the Competition Commission published a report from its Data Services Market Inquiry calling the country's internet data pricing “excessive” (Gilbert,  2019 ).

3. EMPIRICAL APPROACH

We use a sample of 395 s‐year students taking a macroeconomics module in the Economics department to compare the effects of F2F and online learning on students' performance using a range of assessments. The module was an introduction to the application of theoretical economic concepts. The content was both theory‐based (developing economic growth models using concepts and equations) and skill‐based (application involving the collection of data from online data sources and analyzing the data using statistical software). Both individual and group assignments formed part of the assessments. Before the end of the semester, during lockdown in June 2020, we asked the students to complete a survey with questions related to the transition from F2F to online learning and the difficulties that they may have faced. For example, we asked the students: (i) how easy or difficult they found the transition from F2F to online lectures; (ii) what internet options were available to them and which they used the most to access the online prescribed work; (iii) what format of content they accessed and which they preferred the most (i.e. self‐study material in the form of PDF and PowerPoint slides with notes vs. assisted study with narrated slides and lecture recordings); (iv) what difficulties they faced accessing the live online lectures, to name a few. Figure  1 summarizes the key survey questions that we asked the students regarding their transition from F2F to online learning.

Summary of survey data

Before the lockdown, the students had already attended several F2F classes and completed three assessments. We are therefore able to create a dependent variable that is comprised of the average grades of three assignments taken before lockdown and the average grades of three assignments taken after the start of the lockdown for each student. Specifically, we use the difference between the post‐ and pre‐lockdown average grades as the dependent variable. However, the number of student observations dropped to 275 due to some students missing one or more of the assessments. The lecturer, content and format of the assessments remain similar across the module. We estimate the following equation using ordinary least squares (OLS) with robust standard errors:

where Y i is the student's performance measured by the difference between the post and pre‐lockdown average grades. B represents the vector of determinants that measure the difficulty faced by students to transition from F2F to online learning. This vector includes access to the internet, study material preferred, quality of the online live lecture sessions and pre‐lockdown class attendance. X is the vector of student demographic controls such as race, gender and an indicator if the student's perceived family income is below average. The ε i is unobserved student characteristics.

4. ANALYSIS

4.1. descriptive statistics.

Table  2 gives an overview of the sample of students. We find that among the black students, a higher proportion of students reported finding the transition to online learning more difficult. On the other hand, more white students reported finding the transition moderately easy, as did the other races. According to Coetzee ( 2014 ), the quality of schools can vary significantly between higher income and lower‐income areas, with black South Africans far more likely to live in lower‐income areas with lower quality schools than white South Africans. As such, these differences in quality of education from secondary schooling can persist at tertiary level. Furthermore, persistent income inequality between races in South Africa likely means that many poorer black students might not be able to afford wifi connections or large internet data bundles which can make the transition difficult for black students compared to their white counterparts.

Descriptive statistics

Notes : The transition difficulty variable was ordered 1: Very Easy; 2: Moderately Easy; 3: Difficult; and 4: Impossible. Since we have few responses to the extremes, we combined Very Easy and Moderately as well as Difficult and Impossible to make the table easier to read. The table with a full breakdown is available upon request.

A higher proportion of students reported that wifi access made the transition to online learning moderately easy. However, relatively more students reported that mobile internet data and accessing the zero‐rated website made the transition difficult. Surprisingly, not many students made use of the zero‐rated website which was freely available. Figure  2 shows that students who reported difficulty transitioning to online learning did not perform as well in online learning versus F2F when compared to those that found it less difficult to transition.

Transition from F2F to online learning.

Notes : This graph shows the students' responses to the question “How easy did you find the transition from face‐to‐face lectures to online lectures?” in relation to the outcome variable for performance

In Figure  3 , the kernel density shows that students who had access to wifi performed better than those who used mobile internet data or the zero‐rated data.

Access to online learning.

Notes : This graph shows the students' responses to the question “What do you currently use the most to access most of your prescribed work?” in relation to the outcome variable for performance

The regression results are reported in Table  3 . We find that the change in students' performance from F2F to online is negatively associated with the difficulty they faced in transitioning from F2F to online learning. According to student survey responses, factors contributing to difficulty in transitioning included poor internet access, high internet data costs and lack of equipment such as laptops or tablets to access the study materials on the university website. Students who had access to wifi (i.e. fixed wireless broadband, Asymmetric Digital Subscriber Line (ADSL) or optic fiber) performed significantly better, with on average 4.5 points higher grade, in relation to students that had to use mobile internet data (i.e. personal mobile internet data, wifi at home using mobile internet data, or hotspot using mobile internet data) or the zero‐rated website to access the study materials. The insignificant results for the zero‐rated website are surprising given that the website was freely available and did not incur any internet data costs. However, most students in this sample complained that the internet connection on the zero‐rated website was slow, especially in uploading assignments. They also complained about being disconnected when they were in the middle of an assessment. This may have discouraged some students from making use of the zero‐rated website.

Results: Predictors for student performance using the difference on average assessment grades between pre‐ and post‐lockdown

Coefficients reported. Robust standard errors in parentheses.

∗∗∗ p  < .01.

Students who expressed a preference for self‐study approaches (i.e. reading PDF slides or PowerPoint slides with explanatory notes) did not perform as well, on average, as students who preferred assisted study (i.e. listening to recorded narrated slides or lecture recordings). This result is in line with Means et al. ( 2010 ), where student performance was better for online learning that was collaborative or instructor‐driven than in cases where online learners worked independently. Interestingly, we also observe that the performance of students who often attended in‐person classes before the lockdown decreased. Perhaps these students found the F2F lectures particularly helpful in mastering the course material. From the survey responses, we find that a significant proportion of the students (about 70%) preferred F2F to online lectures. This preference for F2F lectures may also be linked to the factors contributing to the difficulty some students faced in transitioning to online learning.

We find that the performance of low‐income students decreased post‐lockdown, which highlights another potential challenge to transitioning to online learning. The picture and sound quality of the live online lectures also contributed to lower performance. Although this result is not statistically significant, it is worth noting as the implications are linked to the quality of infrastructure currently available for students to access online learning. We find no significant effects of race on changes in students' performance, though males appeared to struggle more with the shift to online teaching than females.

For the robustness check in Table  4 , we consider the average grades of the three assignments taken after the start of the lockdown as a dependent variable (i.e. the post‐lockdown average grades for each student). We then include the pre‐lockdown average grades as an explanatory variable. The findings and overall conclusions in Table  4 are consistent with the previous results.

Robustness check: Predictors for student performance using the average assessment grades for post‐lockdown

As a further robustness check in Table  5 , we create a panel for each student across the six assignment grades so we can control for individual heterogeneity. We create a post‐lockdown binary variable that takes the value of 1 for the lockdown period and 0 otherwise. We interact the post‐lockdown dummy variable with a measure for transition difficulty and internet access. The internet access variable is an indicator variable for mobile internet data, wifi, or zero‐rated access to class materials. The variable wifi is a binary variable taking the value of 1 if the student has access to wifi and 0 otherwise. The zero‐rated variable is a binary variable taking the value of 1 if the student used the university's free portal access and 0 otherwise. We also include assignment and student fixed effects. The results in Table  5 remain consistent with our previous findings that students who had wifi access performed significantly better than their peers.

Interaction model

Notes : Coefficients reported. Robust standard errors in parentheses. The dependent variable is the assessment grades for each student on each assignment. The number of observations include the pre‐post number of assessments multiplied by the number of students.

6. CONCLUSION

The Covid‐19 pandemic left many education institutions with no option but to transition to online learning. The University of Pretoria was no exception. We examine the effect of transitioning to online learning on the academic performance of second‐year economic students. We use assessment results from F2F lectures before lockdown, and online lectures post lockdown for the same group of students, together with responses from survey questions. We find that the main contributor to lower academic performance in the online setting was poor internet access, which made transitioning to online learning more difficult. In addition, opting to self‐study (read notes instead of joining online classes and/or watching recordings) did not help the students in their performance.

The implications of the results highlight the need for improved quality of internet infrastructure with affordable internet data pricing. Despite the university's best efforts not to leave any student behind with the zero‐rated website and free monthly internet data, the inequality dynamics in the country are such that invariably some students were negatively affected by this transition, not because the student was struggling academically, but because of inaccessibility of internet (wifi). While the zero‐rated website is a good collaborative initiative between universities and network providers, the infrastructure is not sufficient to accommodate mass students accessing it simultaneously.

This study's findings may highlight some shortcomings in the academic sector that need to be addressed by both the public and private sectors. There is potential for an increase in the digital divide gap resulting from the inequitable distribution of digital infrastructure. This may lead to reinforcement of current inequalities in accessing higher education in the long term. To prepare the country for online learning, some considerations might need to be made to make internet data tariffs more affordable and internet accessible to all. We hope that this study's findings will provide a platform (or will at least start the conversation for taking remedial action) for policy engagements in this regard.

We are aware of some limitations presented by our study. The sample we have at hand makes it difficult to extrapolate our findings to either all students at the University of Pretoria or other higher education students in South Africa. Despite this limitation, our findings highlight the negative effect of the digital divide on students' educational outcomes in the country. The transition to online learning and the high internet data costs in South Africa can also have adverse learning outcomes for low‐income students. With higher education institutions, such as the University of Pretoria, integrating online teaching to overcome the effect of the Covid‐19 pandemic, access to stable internet is vital for students' academic success.

It is also important to note that the data we have at hand does not allow us to isolate wifi's causal effect on students' performance post‐lockdown due to two main reasons. First, wifi access is not randomly assigned; for instance, there is a high chance that students with better‐off family backgrounds might have better access to wifi and other supplementary infrastructure than their poor counterparts. Second, due to the university's data access policy and consent, we could not merge the data at hand with the student's previous year's performance. Therefore, future research might involve examining the importance of these elements to document the causal impact of access to wifi on students' educational outcomes in the country.

ACKNOWLEDGMENT

The authors acknowledge the helpful comments received from the editor, the anonymous reviewers, and Elizabeth Asiedu.

Chisadza, C. , Clance, M. , Mthembu, T. , Nicholls, N. , & Yitbarek, E. (2021). Online and face‐to‐face learning: Evidence from students’ performance during the Covid‐19 pandemic . Afr Dev Rev , 33 , S114–S125. 10.1111/afdr.12520 [ CrossRef ] [ Google Scholar ]

1 https://mybroadband.co.za/news/cellular/309693-mobile-data-prices-south-africa-vs-the-world.html .

2 The 4IR is currently characterized by increased use of new technologies, such as advanced wireless technologies, artificial intelligence, cloud computing, robotics, among others. This era has also facilitated the use of different online learning platforms ( https://www.brookings.edu/research/the-fourth-industrialrevolution-and-digitization-will-transform-africa-into-a-global-powerhouse/ ).

3 Note that we control for income, but it is plausible to assume other unobservable factors such as parental preference and parenting style might also affect access to the internet of students.

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Published on 17.4.2024 in Vol 8 (2024)

Factors Impacting Chinese Older Adults’ Intention to Prevent COVID-19 in the Post–COVID-19 Pandemic Era: Survey Study

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