literature review of online learning during covid 19

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• Review Article
• Published: 27 September 2021

Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap

• Sébastien Goudeau   ORCID: orcid.org/0000-0001-7293-0977 1 ,
• Camille Sanrey   ORCID: orcid.org/0000-0003-3158-1306 1 ,
• Arnaud Stanczak   ORCID: orcid.org/0000-0002-2596-1516 2 ,
• Antony Manstead   ORCID: orcid.org/0000-0001-7540-2096 3 &
• Céline Darnon   ORCID: orcid.org/0000-0003-2613-689X 2  

Nature Human Behaviour volume  5 ,  pages 1273–1281 ( 2021 ) Cite this article

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The COVID-19 pandemic has forced teachers and parents to quickly adapt to a new educational context: distance learning. Teachers developed online academic material while parents taught the exercises and lessons provided by teachers to their children at home. Considering that the use of digital tools in education has dramatically increased during this crisis, and it is set to continue, there is a pressing need to understand the impact of distance learning. Taking a multidisciplinary view, we argue that by making the learning process rely more than ever on families, rather than on teachers, and by getting students to work predominantly via digital resources, school closures exacerbate social class academic disparities. To address this burning issue, we propose an agenda for future research and outline recommendations to help parents, teachers and policymakers to limit the impact of the lockdown on social-class-based academic inequality.

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The widespread effects of the COVID-19 pandemic that emerged in 2019–2020 have drastically increased health, social and economic inequalities 1 , 2 . For more than 900 million learners around the world, the pandemic led to the closure of schools and universities 3 . This exceptional situation forced teachers, parents and students to quickly adapt to a new educational context: distance learning. Teachers had to develop online academic materials that could be used at home to ensure educational continuity while ensuring the necessary physical distancing. Primary and secondary school students suddenly had to work with various kinds of support, which were usually provided online by their teachers. For college students, lockdown often entailed returning to their hometowns while staying connected with their teachers and classmates via video conferences, email and other digital tools. Despite the best efforts of educational institutions, parents and teachers to keep all children and students engaged in learning activities, ensuring educational continuity during school closure—something that is difficult for everyone—may pose unique material and psychological challenges for working-class families and students.

Not only did the pandemic lead to the closure of schools in many countries, often for several weeks, it also accelerated the digitalization of education and amplified the role of parental involvement in supporting the schoolwork of their children. Thus, beyond the specific circumstances of the COVID-19 lockdown, we believe that studying the effects of the pandemic on academic inequalities provides a way to more broadly examine the consequences of school closure and related effects (for example, digitalization of education) on social class inequalities. Indeed, bearing in mind that (1) the risk of further pandemics is higher than ever (that is, we are in a ‘pandemic era’ 4 , 5 ) and (2) beyond pandemics, the use of digital tools in education (and therefore the influence of parental involvement) has dramatically increased during this crisis, and is set to continue, there is a pressing need for an integrative and comprehensive model that examines the consequences of distance learning. Here, we propose such an integrative model that helps us to understand the extent to which the school closures associated with the pandemic amplify economic, digital and cultural divides that in turn affect the psychological functioning of parents, students and teachers in a way that amplifies academic inequalities. Bringing together research in social sciences, ranging from economics and sociology to social, cultural, cognitive and educational psychology, we argue that by getting students to work predominantly via digital resources rather than direct interactions with their teachers, and by making the learning process rely more than ever on families rather than teachers, school closures exacerbate social class academic disparities.

First, we review research showing that social class is associated with unequal access to digital tools, unequal familiarity with digital skills and unequal uses of such tools for learning purposes 6 , 7 . We then review research documenting how unequal familiarity with school culture, knowledge and skills can also contribute to the accentuation of academic inequalities 8 , 9 . Next, we present the results of surveys conducted during the 2020 lockdown showing that the quality and quantity of pedagogical support received from schools varied according to the social class of families (for examples, see refs. 10 , 11 , 12 ). We then argue that these digital, cultural and structural divides represent barriers to the ability of parents to provide appropriate support for children during distance learning (Fig. 1 ). These divides also alter the levels of self-efficacy of parents and children, thereby affecting their engagement in learning activities 13 , 14 . In the final section, we review preliminary evidence for the hypothesis that distance learning widens the social class achievement gap and we propose an agenda for future research. In addition, we outline recommendations that should help parents, teachers and policymakers to use social science research to limit the impact of school closure and distance learning on the social class achievement gap.

Economic, structural, digital and cultural divides influence the psychological functioning of parents and students in a way that amplify inequalities.

The digital divide

Unequal access to digital resources.

Although the use of digital technologies is almost ubiquitous in developed nations, there is a digital divide such that some people are more likely than others to be numerically excluded 15 (Fig. 1 ). Social class is a strong predictor of digital disparities, including the quality of hardware, software and Internet access 16 , 17 , 18 . For example, in 2019, in France, around 1 in 5 working-class families did not have personal access to the Internet compared with less than 1 in 20 of the most privileged families 19 . Similarly, in 2020, in the United Kingdom, 20% of children who were eligible for free school meals did not have access to a computer at home compared with 7% of other children 20 . In 2021, in the United States, 41% of working-class families do not own a laptop or desktop computer and 43% do not have broadband compared with 8% and 7%, respectively, of upper/middle-class Americans 21 . A similar digital gap is also evident between lower-income and higher-income countries 22 .

Second, simply having access to a computer and an Internet connection does not ensure effective distance learning. For example, many of the educational resources sent by teachers need to be printed, thereby requiring access to printers. Moreover, distance learning is more difficult in households with only one shared computer compared with those where each family member has their own 23 . Furthermore, upper/middle-class families are more likely to be able to guarantee a suitable workspace for each child than their working-class counterparts 24 .

In the context of school closures, such disparities are likely to have important consequences for educational continuity. In line with this idea, a survey of approximately 4,000 parents in the United Kingdom confirmed that during lockdown, more than half of primary school children from the poorest families did not have access to their own study space and were less well equipped for distance learning than higher-income families 10 . Similarly, a survey of around 1,300 parents in the Netherlands found that during lockdown, children from working-class families had fewer computers at home and less room to study than upper/middle-class children 11 .

Data from non-Western countries highlight a more general digital divide, showing that developing countries have poorer access to digital equipment. For example, in India in 2018, only 10.7% of households possessed a digital device 25 , while in Pakistan in 2020, 31% of higher-education teachers did not have Internet access and 68.4% did not have a laptop 26 . In general, developing countries lack access to digital technologies 27 , 28 , and these difficulties of access are even greater in rural areas (for example, see ref. 29 ). Consequently, school closures have huge repercussions for the continuity of learning in these countries. For example, in India in 2018, only 11% of the rural and 40% of the urban population above 14 years old could use a computer and access the Internet 25 . Time spent on education during school closure decreased by 80% in Bangladesh 30 . A similar trend was observed in other countries 31 , with only 22% of children engaging in remote learning in Kenya 32 and 50% in Burkina Faso 33 . In Ghana, 26–32% of children spent no time at all on learning during the pandemic 34 . Beyond the overall digital divide, social class disparities are also evident in developing countries, with lower access to digital resources among households in which parental educational levels were low (versus households in which parental educational levels were high; for example, see ref. 35 for Nigeria and ref. 31 for Ecuador).

Unequal digital skills

In addition to unequal access to digital tools, there are also systematic variations in digital skills 36 , 37 (Fig. 1 ). Upper/middle-class families are more familiar with digital tools and resources and are therefore more likely to have the digital skills needed for distance learning 38 , 39 , 40 . These digital skills are particularly useful during school closures, both for students and for parents, for organizing, retrieving and correctly using the resources provided by the teachers (for example, sending or receiving documents by email, printing documents or using word processors).

Social class disparities in digital skills can be explained in part by the fact that children from upper/middle-class families have the opportunity to develop digital skills earlier than working-class families 41 . In member countries of the OECD (Organisation for Economic Co-operation and Development), only 23% of working-class children had started using a computer at the age of 6 years or earlier compared with 43% of upper/middle-class children 42 . Moreover, because working-class people tend to persist less than upper/middle-class people when confronted with digital difficulties 23 , the use of digital tools and resources for distance learning may interfere with the ability of parents to help children with their schoolwork.

Unequal use of digital tools

A third level of digital divide concerns variations in digital tool use 18 , 43 (Fig. 1 ). Upper/middle-class families are more likely to use digital resources for work and education 6 , 41 , 44 , whereas working-class families are more likely to use these resources for entertainment, such as electronic games or social media 6 , 45 . This divide is also observed among students, whereby working-class students tend to use digital technologies for leisure activities, whereas their upper/middle-class peers are more likely to use them for academic activities 46 and to consider that computers and the Internet provide an opportunity for education and training 23 . Furthermore, working-class families appear to regulate the digital practices of their children less 47 and are more likely to allow screens in the bedrooms of children and teenagers without setting limits on times or practices 48 .

In sum, inequalities in terms of digital resources, skills and use have strong implications for distance learning. This is because they make working-class students and parents particularly vulnerable when learning relies on extensive use of digital devices rather than on face-to-face interaction with teachers.

The cultural divide

Even if all three levels of digital divide were closed, upper/middle-class families would still be better prepared than working-class families to ensure educational continuity for their children. Upper/middle-class families are more familiar with the academic knowledge and skills that are expected and valued in educational settings, as well as with the independent, autonomous way of learning that is valued in the school culture and becomes even more important during school closure (Fig. 1 ).

Unequal familiarity with academic knowledge and skills

According to classical social reproduction theory 8 , 49 , school is not a neutral place in which all forms of language and knowledge are equally valued. Academic contexts expect and value culture-specific and taken-for-granted forms of knowledge, skills and ways of being, thinking and speaking that are more in tune with those developed through upper/middle-class socialization (that is, ‘cultural capital’ 8 , 50 , 51 , 52 , 53 ). For instance, academic contexts value interest in the arts, museums and literature 54 , 55 , a type of interest that is more likely to develop through socialization in upper/middle-class families than in working-class socialization 54 , 56 . Indeed, upper/middle-class parents are more likely than working-class parents to engage in activities that develop this cultural capital. For example, they possess more books and cultural objects at home, read more stories to their children and visit museums and libraries more often (for examples, see refs. 51 , 54 , 55 ). Upper/middle-class children are also more involved in extra-curricular activities (for example, playing a musical instrument) than working-class children 55 , 56 , 57 .

Beyond this implicit familiarization with the school curriculum, upper/middle-class parents more often organize educational activities that are explicitly designed to develop academic skills of their children 57 , 58 , 59 . For example, they are more likely to monitor and re-explain lessons or use games and textbooks to develop and reinforce academic skills (for example, labelling numbers, letters or colours 57 , 60 ). Upper/middle-class parents also provide higher levels of support and spend more time helping children with homework than working-class parents (for examples, see refs. 61 , 62 ). Thus, even if all parents are committed to the academic success of their children, working-class parents have fewer chances to provide the help that children need to complete homework 63 , and homework is more beneficial for children from upper-middle class families than for children from working-class families 64 , 65 .

School closures amplify the impact of cultural inequalities

The trends described above have been observed in ‘normal’ times when schools are open. School closures, by making learning rely more strongly on practices implemented at home (rather than at school), are likely to amplify the impact of these disparities. Consistent with this idea, research has shown that the social class achievement gap usually greatly widens during school breaks—a phenomenon described as ‘summer learning loss’ or ‘summer setback’ 66 , 67 , 68 . During holidays, the learning by children tends to decline, and this is particularly pronounced in children from working-class families. Consequently, the social class achievement gap grows more rapidly during the summer months than it does in the rest of the year. This phenomenon is partly explained by the fact that during the break from school, social class disparities in investment in activities that are beneficial for academic achievement (for example, reading, travelling to a foreign country or museum visits) are more pronounced.

Therefore, when they are out of school, children from upper/middle-class backgrounds may continue to develop academic skills unlike their working-class counterparts, who may stagnate or even regress. Research also indicates that learning loss during school breaks tends to be cumulative 66 . Thus, repeated episodes of school closure are likely to have profound consequences for the social class achievement gap. Consistent with the idea that school closures could lead to similar processes as those identified during summer breaks, a recent survey indicated that during the COVID-19 lockdown in the United Kingdom, children from upper/middle-class families spent more time on educational activities (5.8 h per day) than those from working-class families (4.5 h per day) 7 , 69 .

Unequal dispositions for autonomy and self-regulation

School closures have encouraged autonomous work among students. This ‘independent’ way of studying is compatible with the family socialization of upper/middle-class students, but does not match the interdependent norms more commonly associated with working-class contexts 9 . Upper/middle-class contexts tend to promote cultural norms of independence whereby individuals perceive themselves as autonomous actors, independent of other individuals and of the social context, able to pursue their own goals 70 . For example, upper/middle-class parents tend to invite children to express their interests, preferences and opinions during the various activities of everyday life 54 , 55 . Conversely, in working-class contexts characterized by low economic resources and where life is more uncertain, individuals tend to perceive themselves as interdependent, connected to others and members of social groups 53 , 70 , 71 . This interdependent self-construal fits less well with the independent culture of academic contexts. This cultural mismatch between interdependent self-construal common in working-class students and the independent norms of the educational institution has negative consequences for academic performance 9 .

Once again, the impact of these differences is likely to be amplified during school closures, when being able to work alone and autonomously is especially useful. The requirement to work alone is more likely to match the independent self-construal of upper/middle-class students than the interdependent self-construal of working-class students. In the case of working-class students, this mismatch is likely to increase their difficulties in working alone at home. Supporting our argument, recent research has shown that working-class students tend to underachieve in contexts where students work individually compared with contexts where students work with others 72 . Similarly, during school closures, high self-regulation skills (for example, setting goals, selecting appropriate learning strategies and maintaining motivation 73 ) are required to maintain study activities and are likely to be especially useful for using digital resources efficiently. Research has shown that students from working-class backgrounds typically develop their self-regulation skills to a lesser extent than those from upper/middle-class backgrounds 74 , 75 , 76 .

Interestingly, some authors have suggested that independent (versus interdependent) self-construal may also affect communication with teachers 77 . Indeed, in the context of distance learning, working-class families are less likely to respond to the communication of teachers because their ‘interdependent’ self leads them to respect hierarchies, and thus perceive teachers as an expert who ‘can be trusted to make the right decisions for learning’. Upper/middle class families, relying on ‘independent’ self-construal, are more inclined to seek individualized feedback, and therefore tend to participate to a greater extent in exchanges with teachers. Such cultural differences are important because they can also contribute to the difficulties encountered by working-class families.

The structural divide: unequal support from schools

The issues reviewed thus far all increase the vulnerability of children and students from underprivileged backgrounds when schools are closed. To offset these disadvantages, it might be expected that the school should increase its support by providing additional resources for working-class students. However, recent data suggest that differences in the material and human resources invested in providing educational support for children during periods of school closure were—paradoxically—in favour of upper/middle-class students (Fig. 1 ). In England, for example, upper/middle-class parents reported benefiting from online classes and video-conferencing with teachers more often than working-class parents 10 . Furthermore, active help from school (for example, online teaching, private tutoring or chats with teachers) occurred more frequently in the richest households (64% of the richest households declared having received help from school) than in the poorest households (47%). Another survey found that in the United Kingdom, upper/middle-class children were more likely to take online lessons every day (30%) than working-class students (16%) 12 . This substantial difference might be due, at least in part, to the fact that private schools are better equipped in terms of online platforms (60% of schools have at least one online platform) than state schools (37%, and 23% in the most deprived schools) and were more likely to organize daily online lessons. Similarly, in the United Kingdom, in schools with a high proportion of students eligible for free school meals, teachers were less inclined to broadcast an online lesson for their pupils 78 . Interestingly, 58% of teachers in the wealthiest areas reported having messaged their students or their students’ parents during lockdown compared with 47% in the most deprived schools. In addition, the probability of children receiving technical support from the school (for example, by providing pupils with laptops or other devices) is, surprisingly, higher in the most advantaged schools than in the most deprived 78 .

In addition to social class disparities, there has been less support from schools for African-American and Latinx students. During school closures in the United States, 40% of African-American students and 30% of Latinx students received no online teaching compared with 10% of white students 79 . Another source of inequality is that the probability of school closure was correlated with social class and race. In the United States, for example, school closures from September to December 2020 were more common in schools with a high proportion of racial/ethnic minority students, who experience homelessness and are eligible for free/discounted school meals 80 .

Similarly, access to educational resources and support was lower in poorer (compared with richer) countries 81 . In sub-Saharan Africa, during lockdown, 45% of children had no exposure at all to any type of remote learning. Of those who did, the medium was mostly radio, television or paper rather than digital. In African countries, at most 10% of children received some material through the Internet. In Latin America, 90% of children received some remote learning, but less than half of that was through the internet—the remainder being via radio and television 81 . In Ecuador, high-school students from the lowest wealth quartile had fewer remote-learning opportunities, such as Google class/Zoom, than students from the highest wealth quartile 31 .

Thus, the achievement gap and its accentuation during lockdown are due not only to the cultural and digital disadvantages of working-class families but also to unequal support from schools. This inequality in school support is not due to teachers being indifferent to or even supportive of social stratification. Rather, we believe that these effects are fundamentally structural. In many countries, schools located in upper/middle-class neighbourhoods have more money than those in the poorest neighbourhoods. Moreover, upper/middle-class parents invest more in the schools of their children than working-class parents (for example, see ref. 82 ), and schools have an interest in catering more for upper/middle-class families than for working-class families 83 . Additionally, the expectation of teachers may be lower for working-class children 84 . For example, they tend to estimate that working-class students invest less effort in learning than their upper/middle-class counterparts 85 . These differences in perception may have influenced the behaviour of teachers during school closure, such that teachers in privileged neighbourhoods provided more information to students because they expected more from them in term of effort and achievement. The fact that upper/middle-class parents are better able than working-class parents to comply with the expectations of teachers (for examples, see refs. 55 , 86 ) may have reinforced this phenomenon. These discrepancies echo data showing that working-class students tend to request less help in their schoolwork than upper/middle-class ones 87 , and they may even avoid asking for help because they believe that such requests could lead to reprimands 88 . During school closures, these students (and their families) may in consequence have been less likely to ask for help and resources. Jointly, these phenomena have resulted in upper/middle-class families receiving more support from schools during lockdown than their working-class counterparts.

Psychological effects of digital, cultural and structural divides

Despite being strongly influenced by social class, differences in academic achievement are often interpreted by parents, teachers and students as reflecting differences in ability 89 . As a result, upper/middle-class students are usually perceived—and perceive themselves—as smarter than working-class students, who are perceived—and perceive themselves—as less intelligent 90 , 91 , 92 or less able to succeed 93 . Working-class students also worry more about the fact that they might perform more poorly than upper/middle-class students 94 , 95 . These fears influence academic learning in important ways. In particular, they can consume cognitive resources when children and students work on academic tasks 96 , 97 . Self-efficacy also plays a key role in engaging in learning and perseverance in the face of difficulties 13 , 98 . In addition, working-class students are those for whom the fear of being outperformed by others is the most negatively related to academic performance 99 .

The fact that working-class children and students are less familiar with the tasks set by teachers, and less well equipped and supported, makes them more likely to experience feelings of incompetence (Fig. 1 ). Working-class parents are also more likely than their upper/middle-class counterparts to feel unable to help their children with schoolwork. Consistent with this, research has shown that both working-class students and parents have lower feelings of academic self-efficacy than their upper/middle-class counterparts 100 , 101 . These differences have been documented under ‘normal’ conditions but are likely to be exacerbated during distance learning. Recent surveys conducted during the school closures have confirmed that upper/middle-class families felt better able to support their children in distance learning than did working-class families 10 and that upper/middle-class parents helped their children more and felt more capable to do so 11 , 12 .

Pandemic disparity, future directions and recommendations

The research reviewed thus far suggests that children and their families are highly unequal with respect to digital access, skills and use. It also shows that upper/middle-class students are more likely to be supported in their homework (by their parents and teachers) than working-class students, and that upper/middle-class students and parents will probably feel better able than working-class ones to adapt to the context of distance learning. For all these reasons, we anticipate that as a result of school closures, the COVID-19 pandemic will substantially increase the social class achievement gap. Because school closures are a recent occurrence, it is too early to measure with precision their effects on the widening of the achievement gap. However, some recent data are consistent with this idea.

Evidence for a widening gap during the pandemic

Comparing academic achievement in 2020 with previous years provides an early indication of the effects of school closures during the pandemic. In France, for example, first and second graders take national evaluations at the beginning of the school year. Initial comparisons of the results for 2020 with those from previous years revealed that the gap between schools classified as ‘priority schools’ (those in low-income urban areas) and schools in higher-income neighbourhoods—a gap observed every year—was particularly pronounced in 2020 in both French and mathematics 102 .

Similarly, in the Netherlands, national assessments take place twice a year. In 2020, they took place both before and after school closures. A recent analysis compared progress during this period in 2020 in mathematics/arithmetic, spelling and reading comprehension for 7–11-year-old students within the same period in the three previous years 103 . Results indicated a general learning loss in 2020. More importantly, for the 8% of working-class children, the losses were 40% greater than they were for upper/middle-class children.

Similar results were observed in Belgium among students attending the final year of primary school. Compared with students from previous cohorts, students affected by school closures experienced a substantial decrease in their mathematics and language scores, with children from more disadvantaged backgrounds experiencing greater learning losses 104 . Likewise, oral reading assessments in more than 100 school districts in the United States showed that the development of this skill among children in second and third grade significantly slowed between Spring and Autumn 2020, but this slowdown was more pronounced in schools from lower-achieving districts 105 .

It is likely that school closures have also amplified racial disparities in learning and achievement. For example, in the United States, after the first lockdown, students of colour lost the equivalent of 3–5 months of learning, whereas white students were about 1–3 months behind. Moreover, in the Autumn, when some students started to return to classrooms, African-American and Latinx students were more likely to continue distance learning, despite being less likely to have access to the digital tools, Internet access and live contact with teachers 106 .

In some African countries (for example, Ethiopia, Kenya, Liberia, Tanzania and Uganda), the COVID-19 crisis has resulted in learning loss ranging from 6 months to more 1 year 107 , and this learning loss appears to be greater for working-class children (that is, those attending no-fee schools) than for upper/middle-class children 108 .

These findings show that school closures have exacerbated achievement gaps linked to social class and ethnicity. However, more research is needed to address the question of whether school closures differentially affect the learning of students from working- and upper/middle-class families.

Future directions

First, to assess the specific and unique impact of school closures on student learning, longitudinal research should compare student achievement at different times of the year, before, during and after school closures, as has been done to document the summer learning loss 66 , 109 . In the coming months, alternating periods of school closure and opening may occur, thereby presenting opportunities to do such research. This would also make it possible to examine whether the gap diminishes a few weeks after children return to in-school learning or whether, conversely, it increases with time because the foundations have not been sufficiently acquired to facilitate further learning 110 .

Second, the mechanisms underlying the increase in social class disparities during school closures should be examined. As discussed above, school closures result in situations for which students are unevenly prepared and supported. It would be appropriate to seek to quantify the contribution of each of the factors that might be responsible for accentuating the social class achievement gap. In particular, distinguishing between factors that are relatively ‘controllable’ (for example, resources made available to pupils) and those that are more difficult to control (for example, the self-efficacy of parents in supporting the schoolwork of their children) is essential to inform public policy and teaching practices.

Third, existing studies are based on general comparisons and very few provide insights into the actual practices that took place in families during school closure and how these practices affected the achievement gap. For example, research has documented that parents from working-class backgrounds are likely to find it more difficult to help their children to complete homework and to provide constructive feedback 63 , 111 , something that could in turn have a negative impact on the continuity of learning of their children. In addition, it seems reasonable to assume that during lockdown, parents from upper/middle-class backgrounds encouraged their children to engage in practices that, even if not explicitly requested by teachers, would be beneficial to learning (for example, creative activities or reading). Identifying the practices that best predict the maintenance or decline of educational achievement during school closures would help identify levers for intervention.

Finally, it would be interesting to investigate teaching practices during school closures. The lockdown in the spring of 2020 was sudden and unexpected. Within a few days, teachers had to find a way to compensate for the school closure, which led to highly variable practices. Some teachers posted schoolwork on platforms, others sent it by email, some set work on a weekly basis while others set it day by day. Some teachers also set up live sessions in large or small groups, providing remote meetings for questions and support. There have also been variations in the type of feedback given to students, notably through the monitoring and correcting of work. Future studies should examine in more detail what practices schools and teachers used to compensate for the school closures and their effects on widening, maintaining or even reducing the gap, as has been done for certain specific literacy programmes 112 as well as specific instruction topics (for example, ecology and evolution 113 ).

Practical recommendations

We are aware of the debate about whether social science research on COVID-19 is suitable for making policy decisions 114 , and we draw attention to the fact that some of our recommendations (Table 1 ) are based on evidence from experiments or interventions carried out pre-COVID while others are more speculative. In any case, we emphasize that these suggestions should be viewed with caution and be tested in future research. Some of our recommendations could be implemented in the event of new school closures, others only when schools re-open. We also acknowledge that while these recommendations are intended for parents and teachers, their implementation largely depends on the adoption of structural policies. Importantly, given all the issues discussed above, we emphasize the importance of prioritizing, wherever possible, in-person learning over remote learning 115 and where this is not possible, of implementing strong policies to support distance learning, especially for disadvantaged families.

Where face-to face teaching is not possible and teachers are responsible for implementing distance learning, it will be important to make them aware of the factors that can exacerbate inequalities during lockdown and to provide them with guidance about practices that would reduce these inequalities. Thus, there is an urgent need for interventions aimed at making teachers aware of the impact of the social class of children and families on the following factors: (1) access to, familiarity with and use of digital devices; (2) familiarity with academic knowledge and skills; and (3) preparedness to work autonomously. Increasing awareness of the material, cultural and psychological barriers that working-class children and families face during lockdown should increase the quality and quantity of the support provided by teachers and thereby positively affect the achievements of working-class students.

In addition to increasing the awareness of teachers of these barriers, teachers should be encouraged to adjust the way they communicate with working-class families due to differences in self-construal compared with upper/middle-class families 77 . For example, questions about family (rather than personal) well-being would be congruent with interdependent self-construals. This should contribute to better communication and help keep a better track of the progress of students during distance learning.

It is also necessary to help teachers to engage in practices that have a chance of reducing inequalities 53 , 116 . Particularly important is that teachers and schools ensure that homework can be done by all children, for example, by setting up organizations that would help children whose parents are not in a position to monitor or assist with the homework of their children. Options include homework help groups and tutoring by teachers after class. When schools are open, the growing tendency to set homework through digital media should be resisted as far as possible given the evidence we have reviewed above. Moreover, previous research has underscored the importance of homework feedback provided by teachers, which is positively related to the amount of homework completed and predictive of academic performance 117 . Where homework is web-based, it has also been shown that feedback on web-based homework enhances the learning of students 118 . It therefore seems reasonable to predict that the social class achievement gap will increase more slowly (or even remain constant or be reversed) in schools that establish individualized monitoring of students, by means of regular calls and feedback on homework, compared with schools where the support provided to pupils is more generic.

Given that learning during lockdown has increasingly taken place in family settings, we believe that interventions involving the family are also likely to be effective 119 , 120 , 121 . Simply providing families with suitable material equipment may be insufficient. Families should be given training in the efficient use of digital technology and pedagogical support. This would increase the self-efficacy of parents and students, with positive consequences for achievement. Ideally, such training would be delivered in person to avoid problems arising from the digital divide. Where this is not possible, individualized online tutoring should be provided. For example, studies conducted during the lockdown in Botswana and Italy have shown that individual online tutoring directly targeting either parents or students in middle school has a positive impact on the achievement of students, particularly for working-class students 122 , 123 .

Interventions targeting families should also address the psychological barriers faced by working-class families and children. Some interventions have already been designed and been shown to be effective in reducing the social class achievement gap, particularly in mathematics and language 124 , 125 , 126 . For example, research showed that an intervention designed to train low-income parents in how to support the mathematical development of their pre-kindergarten children (including classes and access to a library of kits to use at home) increased the quality of support provided by the parents, with a corresponding impact on the development of mathematical knowledge of their children. Such interventions should be particularly beneficial in the context of school closure.

Beyond its impact on academic performance and inequalities, the COVID-19 crisis has shaken the economies of countries around the world, casting millions of families around the world into poverty 127 , 128 , 129 . As noted earlier, there has been a marked increase in economic inequalities, bringing with it all the psychological and social problems that such inequalities create 130 , 131 , especially for people who live in scarcity 132 . The increase in educational inequalities is just one facet of the many difficulties that working-class families will encounter in the coming years, but it is one that could seriously limit the chances of their children escaping from poverty by reducing their opportunities for upward mobility. In this context, it should be a priority to concentrate resources on the most deprived students. A large proportion of the poorest households do not own a computer and do not have personal access to the Internet, which has important consequences for distance learning. During school closures, it is therefore imperative to provide such families with adequate equipment and Internet service, as was done in some countries in spring 2020. Even if the provision of such equipment is not in itself sufficient, it is a necessary condition for ensuring pedagogical continuity during lockdown.

Finally, after prolonged periods of school closure, many students may not have acquired the skills needed to pursue their education. A possible consequence would be an increase in the number of students for whom teachers recommend class repetitions. Class repetitions are contentious. On the one hand, class repetition more frequently affects working-class children and is not efficient in terms of learning improvement 133 . On the other hand, accepting lower standards of academic achievement or even suspending the practice of repeating a class could lead to pupils pursuing their education without mastering the key abilities needed at higher grades. This could create difficulties in subsequent years and, in this sense, be counterproductive. We therefore believe that the most appropriate way to limit the damage of the pandemic would be to help children catch up rather than allowing them to continue without mastering the necessary skills. As is being done in some countries, systematic remedial courses (for example, summer learning programmes) should be organized and financially supported following periods of school closure, with priority given to pupils from working-class families. Such interventions have genuine potential in that research has shown that participation in remedial summer programmes is effective in reducing learning loss during the summer break 134 , 135 , 136 . For example, in one study 137 , 438 students from high-poverty schools were offered a multiyear summer school programme that included various pedagogical and enrichment activities (for example, science investigation and music) and were compared with a ‘no-treatment’ control group. Students who participated in the summer programme progressed more than students in the control group. A meta-analysis 138 of 41 summer learning programmes (that is, classroom- and home-based summer interventions) involving children from kindergarten to grade 8 showed that these programmes had significantly larger benefits for children from working-class families. Although such measures are costly, the cost is small compared to the price of failing to fulfil the academic potential of many students simply because they were not born into upper/middle-class families.

The unprecedented nature of the current pandemic means that we lack strong data on what the school closure period is likely to produce in terms of learning deficits and the reproduction of social inequalities. However, the research discussed in this article suggests that there are good reasons to predict that this period of school closures will accelerate the reproduction of social inequalities in educational achievement.

By making school learning less dependent on teachers and more dependent on families and digital tools and resources, school closures are likely to greatly amplify social class inequalities. At a time when many countries are experiencing second, third or fourth waves of the pandemic, resulting in fresh periods of local or general lockdowns, systematic efforts to test these predictions are urgently needed along with steps to reduce the impact of school closures on the social class achievement gap.

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We thank G. Reis for editing the figure. The writing of this manuscript was supported by grant ANR-19-CE28-0007–PRESCHOOL from the French National Research Agency (S.G.).

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Goudeau, S., Sanrey, C., Stanczak, A. et al. Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap. Nat Hum Behav 5 , 1273–1281 (2021). https://doi.org/10.1038/s41562-021-01212-7

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Research Article

COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis

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

¶ ‡ JZ and YD are contributed equally to this work as first authors.

Affiliation School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Affiliations School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China, Hangzhou Zhongce Vocational School Qiantang, Hangzhou, Zhejiang, China

Roles Data curation, Writing – original draft

Roles Data curation

Roles Writing – original draft

Affiliation Faculty of Education, Shenzhen University, Shenzhen, Guangdong, China

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected] (JH); [email protected] (YZ)

• Junyi Zhang, 
• Yigang Ding, 
• Xinru Yang, 
• Jinping Zhong, 
• XinXin Qiu, 
• Zhishan Zou, 
• Yujie Xu, 
• Xiunan Jin, 
• Xiaomin Wu, 

• Published: August 23, 2022
• https://doi.org/10.1371/journal.pone.0273016
• Reader Comments

The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students’ online learning behavior before and after the outbreak. We collected review data from China’s massive open online course platform called icourse.163 and performed social network analysis on 15 courses to explore courses’ interaction characteristics before, during, and after the COVID-19 pan-demic. Specifically, we focused on the following aspects: (1) variations in the scale of online learning amid COVID-19; (2a) the characteristics of online learning interaction during the pandemic; (2b) the characteristics of online learning interaction after the pandemic; and (3) differences in the interaction characteristics of social science courses and natural science courses. Results revealed that only a small number of courses witnessed an uptick in online interaction, suggesting that the pandemic’s role in promoting the scale of courses was not significant. During the pandemic, online learning interaction became more frequent among course network members whose interaction scale increased. After the pandemic, although the scale of interaction declined, online learning interaction became more effective. The scale and level of interaction in Electrodynamics (a natural science course) and Economics (a social science course) both rose during the pan-demic. However, long after the pandemic, the Economics course sustained online interaction whereas interaction in the Electrodynamics course steadily declined. This discrepancy could be due to the unique characteristics of natural science courses and social science courses.

Citation: Zhang J, Ding Y, Yang X, Zhong J, Qiu X, Zou Z, et al. (2022) COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis. PLoS ONE 17(8): e0273016. https://doi.org/10.1371/journal.pone.0273016

Editor: Heng Luo, Central China Normal University, CHINA

Received: April 20, 2022; Accepted: July 29, 2022; Published: August 23, 2022

Copyright: © 2022 Zhang 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: The data underlying the results presented in the study were downloaded from https://www.icourse163.org/ and are now shared fully on Github ( https://github.com/zjyzhangjunyi/dataset-from-icourse163-for-SNA ). These data have no private information and can be used for academic research free of charge.

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

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

1. Introduction

The development of the mobile internet has spurred rapid advances in online learning, offering novel prospects for teaching and learning and a learning experience completely different from traditional instruction. Online learning harnesses the advantages of network technology and multimedia technology to transcend the boundaries of conventional education [ 1 ]. Online courses have become a popular learning mode owing to their flexibility and openness. During online learning, teachers and students are in different physical locations but interact in multiple ways (e.g., via online forum discussions and asynchronous group discussions). An analysis of online learning therefore calls for attention to students’ participation. Alqurashi [ 2 ] defined interaction in online learning as the process of constructing meaningful information and thought exchanges between more than two people; such interaction typically occurs between teachers and learners, learners and learners, and the course content and learners.

Massive open online courses (MOOCs), a 21st-century teaching mode, have greatly influenced global education. Data released by China’s Ministry of Education in 2020 show that the country ranks first globally in the number and scale of higher education MOOCs. The COVID-19 outbreak has further propelled this learning mode, with universities being urged to leverage MOOCs and other online resource platforms to respond to government’s “School’s Out, But Class’s On” policy [ 3 ]. Besides MOOCs, to reduce in-person gatherings and curb the spread of COVID-19, various online learning methods have since become ubiquitous [ 4 ]. Though Lederman asserted that the COVID-19 outbreak has positioned online learning technologies as the best way for teachers and students to obtain satisfactory learning experiences [ 5 ], it remains unclear whether the COVID-19 pandemic has encouraged interaction in online learning, as interactions between students and others play key roles in academic performance and largely determine the quality of learning experiences [ 6 ]. Similarly, it is also unclear what impact the COVID-19 pandemic has had on the scale of online learning.

Social constructivism paints learning as a social phenomenon. As such, analyzing the social structures or patterns that emerge during the learning process can shed light on learning-based interaction [ 7 ]. Social network analysis helps to explain how a social network, rooted in interactions between learners and their peers, guides individuals’ behavior, emotions, and outcomes. This analytical approach is especially useful for evaluating interactive relationships between network members [ 8 ]. Mohammed cited social network analysis (SNA) as a method that can provide timely information about students, learning communities and interactive networks. SNA has been applied in numerous fields, including education, to identify the number and characteristics of interelement relationships. For example, Lee et al. also used SNA to explore the effects of blogs on peer relationships [ 7 ]. Therefore, adopting SNA to examine interactions in online learning communities during the COVID-19 pandemic can uncover potential issues with this online learning model.

Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, focusing on learners’ interaction characteristics before, during, and after the COVID-19 outbreak. We visually assessed changes in the scale of network interaction before, during, and after the outbreak along with the characteristics of interaction in Gephi. Examining students’ interactions in different courses revealed distinct interactive network characteristics, the pandemic’s impact on online courses, and relevant suggestions. Findings are expected to promote effective interaction and deep learning among students in addition to serving as a reference for the development of other online learning communities.

2. Literature review and research questions

Interaction is deemed as central to the educational experience and is a major focus of research on online learning. Moore began to study the problem of interaction in distance education as early as 1989. He defined three core types of interaction: student–teacher, student–content, and student–student [ 9 ]. Lear et al. [ 10 ] described an interactivity/ community-process model of distance education: they specifically discussed the relationships between interactivity, community awareness, and engaging learners and found interactivity and community awareness to be correlated with learner engagement. Zulfikar et al. [ 11 ] suggested that discussions initiated by the students encourage more students’ engagement than discussions initiated by the instructors. It is most important to afford learners opportunities to interact purposefully with teachers, and improving the quality of learner interaction is crucial to fostering profound learning [ 12 ]. Interaction is an important way for learners to communicate and share information, and a key factor in the quality of online learning [ 13 ].

Timely feedback is the main component of online learning interaction. Woo and Reeves discovered that students often become frustrated when they fail to receive prompt feedback [ 14 ]. Shelley et al. conducted a three-year study of graduate and undergraduate students’ satisfaction with online learning at universities and found that interaction with educators and students is the main factor affecting satisfaction [ 15 ]. Teachers therefore need to provide students with scoring justification, support, and constructive criticism during online learning. Some researchers examined online learning during the COVID-19 pandemic. They found that most students preferred face-to-face learning rather than online learning due to obstacles faced online, such as a lack of motivation, limited teacher-student interaction, and a sense of isolation when learning in different times and spaces [ 16 , 17 ]. However, it can be reduced by enhancing the online interaction between teachers and students [ 18 ].

Research showed that interactions contributed to maintaining students’ motivation to continue learning [ 19 ]. Baber argued that interaction played a key role in students’ academic performance and influenced the quality of the online learning experience [ 20 ]. Hodges et al. maintained that well-designed online instruction can lead to unique teaching experiences [ 21 ]. Banna et al. mentioned that using discussion boards, chat sessions, blogs, wikis, and other tools could promote student interaction and improve participation in online courses [ 22 ]. During the COVID-19 pandemic, Mahmood proposed a series of teaching strategies suitable for distance learning to improve its effectiveness [ 23 ]. Lapitan et al. devised an online strategy to ease the transition from traditional face-to-face instruction to online learning [ 24 ]. The preceding discussion suggests that online learning goes beyond simply providing learning resources; teachers should ideally design real-life activities to give learners more opportunities to participate.

As mentioned, COVID-19 has driven many scholars to explore the online learning environment. However, most have ignored the uniqueness of online learning during this time and have rarely compared pre- and post-pandemic online learning interaction. Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, centering on student interaction before and after the pandemic. Gephi was used to visually analyze changes in the scale and characteristics of network interaction. The following questions were of particular interest:

• (1) Can the COVID-19 pandemic promote the expansion of online learning?
• (2a) What are the characteristics of online learning interaction during the pandemic?
• (2b) What are the characteristics of online learning interaction after the pandemic?
• (3) How do interaction characteristics differ between social science courses and natural science courses?

3. Methodology

3.1 research context.

We selected several courses with a large number of participants and extensive online interaction among hundreds of courses on the icourse.163 MOOC platform. These courses had been offered on the platform for at least three semesters, covering three periods (i.e., before, during, and after the COVID-19 outbreak). To eliminate the effects of shifts in irrelevant variables (e.g., course teaching activities), we chose several courses with similar teaching activities and compared them on multiple dimensions. All course content was taught online. The teachers of each course posted discussion threads related to learning topics; students were expected to reply via comments. Learners could exchange ideas freely in their responses in addition to asking questions and sharing their learning experiences. Teachers could answer students’ questions as well. Conversations in the comment area could partly compensate for a relative absence of online classroom interaction. Teacher–student interaction is conducive to the formation of a social network structure and enabled us to examine teachers’ and students’ learning behavior through SNA. The comment areas in these courses were intended for learners to construct knowledge via reciprocal communication. Meanwhile, by answering students’ questions, teachers could encourage them to reflect on their learning progress. These courses’ successive terms also spanned several phases of COVID-19, allowing us to ascertain the pandemic’s impact on online learning.

3.2 Data collection and preprocessing

To avoid interference from invalid or unclear data, the following criteria were applied to select representative courses: (1) generality (i.e., public courses and professional courses were chosen from different schools across China); (2) time validity (i.e., courses were held before during, and after the pandemic); and (3) notability (i.e., each course had at least 2,000 participants). We ultimately chose 15 courses across the social sciences and natural sciences (see Table 1 ). The coding is used to represent the course name.

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https://doi.org/10.1371/journal.pone.0273016.t001

To discern courses’ evolution during the pandemic, we gathered data on three terms before, during, and after the COVID-19 outbreak in addition to obtaining data from two terms completed well before the pandemic and long after. Our final dataset comprised five sets of interactive data. Finally, we collected about 120,000 comments for SNA. Because each course had a different start time—in line with fluctuations in the number of confirmed COVID-19 cases in China and the opening dates of most colleges and universities—we divided our sample into five phases: well before the pandemic (Phase I); before the pandemic (Phase Ⅱ); during the pandemic (Phase Ⅲ); after the pandemic (Phase Ⅳ); and long after the pandemic (Phase Ⅴ). We sought to preserve consistent time spans to balance the amount of data in each period ( Fig 1 ).

https://doi.org/10.1371/journal.pone.0273016.g001

3.3 Instrumentation

Participants’ comments and “thumbs-up” behavior data were converted into a network structure and compared using social network analysis (SNA). Network analysis, according to M’Chirgui, is an effective tool for clarifying network relationships by employing sophisticated techniques [ 25 ]. Specifically, SNA can help explain the underlying relationships among team members and provide a better understanding of their internal processes. Yang and Tang used SNA to discuss the relationship between team structure and team performance [ 26 ]. Golbeck argued that SNA could improve the understanding of students’ learning processes and reveal learners’ and teachers’ role dynamics [ 27 ].

To analyze Question (1), the number of nodes and diameter in the generated network were deemed as indicators of changes in network size. Social networks are typically represented as graphs with nodes and degrees, and node count indicates the sample size [ 15 ]. Wellman et al. proposed that the larger the network scale, the greater the number of network members providing emotional support, goods, services, and companionship [ 28 ]. Jan’s study measured the network size by counting the nodes which represented students, lecturers, and tutors [ 29 ]. Similarly, network nodes in the present study indicated how many learners and teachers participated in the course, with more nodes indicating more participants. Furthermore, we investigated the network diameter, a structural feature of social networks, which is a common metric for measuring network size in SNA [ 30 ]. The network diameter refers to the longest path between any two nodes in the network. There has been evidence that a larger network diameter leads to greater spread of behavior [ 31 ]. Likewise, Gašević et al. found that larger networks were more likely to spread innovative ideas about educational technology when analyzing MOOC-related research citations [ 32 ]. Therefore, we employed node count and network diameter to measure the network’s spatial size and further explore the expansion characteristic of online courses. Brief introduction of these indicators can be summarized in Table 2 .

https://doi.org/10.1371/journal.pone.0273016.t002

To address Question (2), a list of interactive analysis metrics in SNA were introduced to scrutinize learners’ interaction characteristics in online learning during and after the pandemic, as shown below:

• (1) The average degree reflects the density of the network by calculating the average number of connections for each node. As Rong and Xu suggested, the average degree of a network indicates how active its participants are [ 33 ]. According to Hu, a higher average degree implies that more students are interacting directly with each other in a learning context [ 34 ]. The present study inherited the concept of the average degree from these previous studies: the higher the average degree, the more frequent the interaction between individuals in the network.
• (2) Essentially, a weighted average degree in a network is calculated by multiplying each degree by its respective weight, and then taking the average. Bydžovská took the strength of the relationship into account when determining the weighted average degree [ 35 ]. By calculating friendship’s weighted value, Maroulis assessed peer achievement within a small-school reform [ 36 ]. Accordingly, we considered the number of interactions as the weight of the degree, with a higher average degree indicating more active interaction among learners.
• (3) Network density is the ratio between actual connections and potential connections in a network. The more connections group members have with each other, the higher the network density. In SNA, network density is similar to group cohesion, i.e., a network of more strong relationships is more cohesive [ 37 ]. Network density also reflects how much all members are connected together [ 38 ]. Therefore, we adopted network density to indicate the closeness among network members. Higher network density indicates more frequent interaction and closer communication among students.
• (4) Clustering coefficient describes local network attributes and indicates that two nodes in the network could be connected through adjacent nodes. The clustering coefficient measures users’ tendency to gather (cluster) with others in the network: the higher the clustering coefficient, the more frequently users communicate with other group members. We regarded this indicator as a reflection of the cohesiveness of the group [ 39 ].
• (5) In a network, the average path length is the average number of steps along the shortest paths between any two nodes. Oliveres has observed that when an average path length is small, the route from one node to another is shorter when graphed [ 40 ]. This is especially true in educational settings where students tend to become closer friends. So we consider that the smaller the average path length, the greater the possibility of interaction between individuals in the network.
• (6) A network with a large number of nodes, but whose average path length is surprisingly small, is known as the small-world effect [ 41 ]. A higher clustering coefficient and shorter average path length are important indicators of a small-world network: a shorter average path length enables the network to spread information faster and more accurately; a higher clustering coefficient can promote frequent knowledge exchange within the group while boosting the timeliness and accuracy of knowledge dissemination [ 42 ]. Brief introduction of these indicators can be summarized in Table 3 .

https://doi.org/10.1371/journal.pone.0273016.t003

To analyze Question 3, we used the concept of closeness centrality, which determines how close a vertex is to others in the network. As Opsahl et al. explained, closeness centrality reveals how closely actors are coupled with their entire social network [ 43 ]. In order to analyze social network-based engineering education, Putnik et al. examined closeness centrality and found that it was significantly correlated with grades [ 38 ]. We used closeness centrality to measure the position of an individual in the network. Brief introduction of these indicators can be summarized in Table 4 .

https://doi.org/10.1371/journal.pone.0273016.t004

3.4 Ethics statement

This study was approved by the Academic Committee Office (ACO) of South China Normal University ( http://fzghb.scnu.edu.cn/ ), Guangzhou, China. Research data were collected from the open platform and analyzed anonymously. There are thus no privacy issues involved in this study.

4.1 COVID-19’s role in promoting the scale of online courses was not as important as expected

As shown in Fig 2 , the number of course participants and nodes are closely correlated with the pandemic’s trajectory. Because the number of participants in each course varied widely, we normalized the number of participants and nodes to more conveniently visualize course trends. Fig 2 depicts changes in the chosen courses’ number of participants and nodes before the pandemic (Phase II), during the pandemic (Phase III), and after the pandemic (Phase IV). The number of participants in most courses during the pandemic exceeded those before and after the pandemic. But the number of people who participate in interaction in some courses did not increase.

https://doi.org/10.1371/journal.pone.0273016.g002

In order to better analyze the trend of interaction scale in online courses before, during, and after the pandemic, the selected courses were categorized according to their scale change. When the number of participants increased (decreased) beyond 20% (statistical experience) and the diameter also increased (decreased), the course scale was determined to have increased (decreased); otherwise, no significant change was identified in the course’s interaction scale. Courses were subsequently divided into three categories: increased interaction scale, decreased interaction scale, and no significant change. Results appear in Table 5 .

https://doi.org/10.1371/journal.pone.0273016.t005

From before the pandemic until it broke out, the interaction scale of five courses increased, accounting for 33.3% of the full sample; one course’s interaction scale declined, accounting for 6.7%. The interaction scale of nine courses decreased, accounting for 60%. The pandemic’s role in promoting online courses thus was not as important as anticipated, and most courses’ interaction scale did not change significantly throughout.

No courses displayed growing interaction scale after the pandemic: the interaction scale of nine courses fell, accounting for 60%; and the interaction scale of six courses did not shift significantly, accounting for 40%. Courses with an increased scale of interaction during the pandemic did not maintain an upward trend. On the contrary, the improvement in the pandemic caused learners’ enthusiasm for online learning to wane. We next analyzed several interaction metrics to further explore course interaction during different pandemic periods.

4.2 Characteristics of online learning interaction amid COVID-19

4.2.1 during the covid-19 pandemic, online learning interaction in some courses became more active..

Changes in course indicators with the growing interaction scale during the pandemic are presented in Fig 3 , including SS5, SS6, NS1, NS3, and NS8. The horizontal ordinate indicates the number of courses, with red color representing the rise of the indicator value on the vertical ordinate and blue representing the decline.

https://doi.org/10.1371/journal.pone.0273016.g003

Specifically: (1) The average degree and weighted average degree of the five course networks demonstrated an upward trend. The emergence of the pandemic promoted students’ enthusiasm; learners were more active in the interactive network. (2) Fig 3 shows that 3 courses had increased network density and 2 courses had decreased. The higher the network density, the more communication within the team. Even though the pandemic accelerated the interaction scale and frequency, the tightness between learners in some courses did not improve. (3) The clustering coefficient of social science courses rose whereas the clustering coefficient and small-world property of natural science courses fell. The higher the clustering coefficient and the small-world property, the better the relationship between adjacent nodes and the higher the cohesion [ 39 ]. (4) Most courses’ average path length increased as the interaction scale increased. However, when the average path length grew, adverse effects could manifest: communication between learners might be limited to a small group without multi-directional interaction.

When the pandemic emerged, the only declining network scale belonged to a natural science course (NS2). The change in each course index is pictured in Fig 4 . The abscissa indicates the size of the value, with larger values to the right. The red dot indicates the index value before the pandemic; the blue dot indicates its value during the pandemic. If the blue dot is to the right of the red dot, then the value of the index increased; otherwise, the index value declined. Only the weighted average degree of the course network increased. The average degree, network density decreased, indicating that network members were not active and that learners’ interaction degree and communication frequency lessened. Despite reduced learner interaction, the average path length was small and the connectivity between learners was adequate.

https://doi.org/10.1371/journal.pone.0273016.g004

4.2.2 After the COVID-19 pandemic, the scale decreased rapidly, but most course interaction was more effective.

Fig 5 shows the changes in various courses’ interaction indicators after the pandemic, including SS1, SS2, SS3, SS6, SS7, NS2, NS3, NS7, and NS8.

https://doi.org/10.1371/journal.pone.0273016.g005

Specifically: (1) The average degree and weighted average degree of most course networks decreased. The scope and intensity of interaction among network members declined rapidly, as did learners’ enthusiasm for communication. (2) The network density of seven courses also fell, indicating weaker connections between learners in most courses. (3) In addition, the clustering coefficient and small-world property of most course networks decreased, suggesting little possibility of small groups in the network. The scope of interaction between learners was not limited to a specific space, and the interaction objects had no significant tendencies. (4) Although the scale of course interaction became smaller in this phase, the average path length of members’ social networks shortened in nine courses. Its shorter average path length would expedite the spread of information within the network as well as communication and sharing among network members.

Fig 6 displays the evolution of course interaction indicators without significant changes in interaction scale after the pandemic, including SS4, SS5, NS1, NS4, NS5, and NS6.

https://doi.org/10.1371/journal.pone.0273016.g006

Specifically: (1) Some course members’ social networks exhibited an increase in the average and weighted average. In these cases, even though the course network’s scale did not continue to increase, communication among network members rose and interaction became more frequent and deeper than before. (2) Network density and average path length are indicators of social network density. The greater the network density, the denser the social network; the shorter the average path length, the more concentrated the communication among network members. However, at this phase, the average path length and network density in most courses had increased. Yet the network density remained small despite having risen ( Table 6 ). Even with more frequent learner interaction, connections remained distant and the social network was comparatively sparse.

https://doi.org/10.1371/journal.pone.0273016.t006

In summary, the scale of interaction did not change significantly overall. Nonetheless, some course members’ frequency and extent of interaction increased, and the relationships between network members became closer as well. In the study, we found it interesting that the interaction scale of Economics (a social science course) course and Electrodynamics (a natural science course) course expanded rapidly during the pandemic and retained their interaction scale thereafter. We next assessed these two courses to determine whether their level of interaction persisted after the pandemic.

4.3 Analyses of natural science courses and social science courses

4.3.1 analyses of the interaction characteristics of economics and electrodynamics..

Economics and Electrodynamics are social science courses and natural science courses, respectively. Members’ interaction within these courses was similar: the interaction scale increased significantly when COVID-19 broke out (Phase Ⅲ), and no significant changes emerged after the pandemic (Phase Ⅴ). We hence focused on course interaction long after the outbreak (Phase V) and compared changes across multiple indicators, as listed in Table 7 .

https://doi.org/10.1371/journal.pone.0273016.t007

As the pandemic continued to improve, the number of participants and the diameter long after the outbreak (Phase V) each declined for Economics compared with after the pandemic (Phase IV). The interaction scale decreased, but the interaction between learners was much deeper. Specifically: (1) The weighted average degree, network density, clustering coefficient, and small-world property each reflected upward trends. The pandemic therefore exerted a strong impact on this course. Interaction was well maintained even after the pandemic. The smaller network scale promoted members’ interaction and communication. (2) Compared with after the pandemic (Phase IV), members’ network density increased significantly, showing that relationships between learners were closer and that cohesion was improving. (3) At the same time, as the clustering coefficient and small-world property grew, network members demonstrated strong small-group characteristics: the communication between them was deepening and their enthusiasm for interaction was higher. (4) Long after the COVID-19 outbreak (Phase V), the average path length was reduced compared with previous terms, knowledge flowed more quickly among network members, and the degree of interaction gradually deepened.

The average degree, weighted average degree, network density, clustering coefficient, and small-world property of Electrodynamics all decreased long after the COVID-19 outbreak (Phase V) and were lower than during the outbreak (Phase Ⅲ). The level of learner interaction therefore gradually declined long after the outbreak (Phase V), and connections between learners were no longer active. Although the pandemic increased course members’ extent of interaction, this rise was merely temporary: students’ enthusiasm for learning waned rapidly and their interaction decreased after the pandemic (Phase IV). To further analyze the interaction characteristics of course members in Economics and Electrodynamics, we evaluated the closeness centrality of their social networks, as shown in section 4.3.2.

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

The change in the closeness centrality of social networks in Economics was small, and no sharp upward trend appeared during the pandemic outbreak, as shown in Fig 7 . The emergence of COVID-19 apparently fostered learners’ interaction in Economics albeit without a significant impact. The closeness centrality changed in Electrodynamics varied from that of Economics: upon the COVID-19 outbreak, closeness centrality was significantly different from other semesters. Communication between learners was closer and interaction was more effective. Electrodynamics course members’ social network proximity decreased rapidly after the pandemic. Learners’ communication lessened. In general, Economics course showed better interaction before the outbreak and was less affected by the pandemic; Electrodynamics course was more affected by the pandemic and showed different interaction characteristics at different periods of the pandemic.

(Note: "****" indicates the significant distinction in closeness centrality between the two periods, otherwise no significant distinction).

https://doi.org/10.1371/journal.pone.0273016.g007

5. Discussion

We referred to discussion forums from several courses on the icourse.163 MOOC platform to compare online learning before, during, and after the COVID-19 pandemic via SNA and to delineate the pandemic’s effects on online courses. Only 33.3% of courses in our sample increased in terms of interaction during the pandemic; the scale of interaction did not rise in any courses thereafter. When the courses scale rose, the scope and frequency of interaction showed upward trends during the pandemic; and the clustering coefficient of natural science courses and social science courses differed: the coefficient for social science courses tended to rise whereas that for natural science courses generally declined. When the pandemic broke out, the interaction scale of a single natural science course decreased along with its interaction scope and frequency. The amount of interaction in most courses shrank rapidly during the pandemic and network members were not as active as they had been before. However, after the pandemic, some courses saw declining interaction but greater communication between members; interaction also became more frequent and deeper than before.

5.1 During the COVID-19 pandemic, the scale of interaction increased in only a few courses

The pandemic outbreak led to a rapid increase in the number of participants in most courses; however, the change in network scale was not significant. The scale of online interaction expanded swiftly in only a few courses; in others, the scale either did not change significantly or displayed a downward trend. After the pandemic, the interaction scale in most courses decreased quickly; the same pattern applied to communication between network members. Learners’ enthusiasm for online interaction reduced as the circumstances of the pandemic improved—potentially because, during the pandemic, China’s Ministry of Education declared “School’s Out, But Class’s On” policy. Major colleges and universities were encouraged to use the Internet and informational resources to provide learning support, hence the sudden increase in the number of participants and interaction in online courses [ 46 ]. After the pandemic, students’ enthusiasm for online learning gradually weakened, presumably due to easing of the pandemic [ 47 ]. More activities also transitioned from online to offline, which tempered learners’ online discussion. Research has shown that long-term online learning can even bore students [ 48 ].

Most courses’ interaction scale decreased significantly after the pandemic. First, teachers and students occupied separate spaces during the outbreak, had few opportunities for mutual cooperation and friendship, and lacked a sense of belonging [ 49 ]. Students’ enthusiasm for learning dissipated over time [ 50 ]. Second, some teachers were especially concerned about adapting in-person instructional materials for digital platforms; their pedagogical methods were ineffective, and they did not provide learning activities germane to student interaction [ 51 ]. Third, although teachers and students in remote areas were actively engaged in online learning, some students could not continue to participate in distance learning due to inadequate technology later in the outbreak [ 52 ].

5.2 Characteristics of online learning interaction during and after the COVID-19 pandemic

5.2.1 during the covid-19 pandemic, online interaction in most courses did not change significantly..

The interaction scale of only a few courses increased during the pandemic. The interaction scope and frequency of these courses climbed as well. Yet even as the degree of network interaction rose, course network density did not expand in all cases. The pandemic sparked a surge in the number of online learners and a rapid increase in network scale, but students found it difficult to interact with all learners. Yau pointed out that a greater network scale did not enrich the range of interaction between individuals; rather, the number of individuals who could interact directly was limited [ 53 ]. The internet facilitates interpersonal communication. However, not everyone has the time or ability to establish close ties with others [ 54 ].

In addition, social science courses and natural science courses in our sample revealed disparate trends in this regard: the clustering coefficient of social science courses increased and that of natural science courses decreased. Social science courses usually employ learning approaches distinct from those in natural science courses [ 55 ]. Social science courses emphasize critical and innovative thinking along with personal expression [ 56 ]. Natural science courses focus on practical skills, methods, and principles [ 57 ]. Therefore, the content of social science courses can spur large-scale discussion among learners. Some course evaluations indicated that the course content design was suboptimal as well: teachers paid close attention to knowledge transmission and much less to piquing students’ interest in learning. In addition, the thread topics that teachers posted were scarcely diversified and teachers’ questions lacked openness. These attributes could not spark active discussion among learners.

5.2.2 Online learning interaction declined after the COVID-19 pandemic.

Most courses’ interaction scale and intensity decreased rapidly after the pandemic, but some did not change. Courses with a larger network scale did not continue to expand after the outbreak, and students’ enthusiasm for learning paled. The pandemic’s reduced severity also influenced the number of participants in online courses. Meanwhile, restored school order moved many learning activities from virtual to in-person spaces. Face-to-face learning has gradually replaced online learning, resulting in lower enrollment and less interaction in online courses. Prolonged online courses could have also led students to feel lonely and to lack a sense of belonging [ 58 ].

The scale of interaction in some courses did not change substantially after the pandemic yet learners’ connections became tighter. We hence recommend that teachers seize pandemic-related opportunities to design suitable activities. Additionally, instructors should promote student-teacher and student-student interaction, encourage students to actively participate online, and generally intensify the impact of online learning.

5.3 What are the characteristics of interaction in social science courses and natural science courses?

The level of interaction in Economics (a social science course) was significantly higher than that in Electrodynamics (a natural science course), and the small-world property in Economics increased as well. To boost online courses’ learning-related impacts, teachers can divide groups of learners based on the clustering coefficient and the average path length. Small groups of students may benefit teachers in several ways: to participate actively in activities intended to expand students’ knowledge, and to serve as key actors in these small groups. Cultivating students’ keenness to participate in class activities and self-management can also help teachers guide learner interaction and foster deep knowledge construction.

As evidenced by comments posted in the Electrodynamics course, we observed less interaction between students. Teachers also rarely urged students to contribute to conversations. These trends may have arisen because teachers and students were in different spaces. Teachers might have struggled to discern students’ interaction status. Teachers could also have failed to intervene in time, to design online learning activities that piqued learners’ interest, and to employ sound interactive theme planning and guidance. Teachers are often active in traditional classroom settings. Their roles are comparatively weakened online, such that they possess less control over instruction [ 59 ]. Online instruction also requires a stronger hand in learning: teachers should play a leading role in regulating network members’ interactive communication [ 60 ]. Teachers can guide learners to participate, help learners establish social networks, and heighten students’ interest in learning [ 61 ]. Teachers should attend to core members in online learning while also considering edge members; by doing so, all network members can be driven to share their knowledge and become more engaged. Finally, teachers and assistant teachers should help learners develop knowledge, exchange topic-related ideas, pose relevant questions during course discussions, and craft activities that enable learners to interact online [ 62 ]. These tactics can improve the effectiveness of online learning.

As described, network members displayed distinct interaction behavior in Economics and Electrodynamics courses. First, these courses varied in their difficulty: the social science course seemed easier to understand and focused on divergent thinking. Learners were often willing to express their views in comments and to ponder others’ perspectives [ 63 ]. The natural science course seemed more demanding and was oriented around logical thinking and skills [ 64 ]. Second, courses’ content differed. In general, social science courses favor the acquisition of declarative knowledge and creative knowledge compared with natural science courses. Social science courses also entertain open questions [ 65 ]. Natural science courses revolve around principle knowledge, strategic knowledge, and transfer knowledge [ 66 ]. Problems in these courses are normally more complicated than those in social science courses. Third, the indicators affecting students’ attitudes toward learning were unique. Guo et al. discovered that “teacher feedback” most strongly influenced students’ attitudes towards learning social science courses but had less impact on students in natural science courses [ 67 ]. Therefore, learners in social science courses likely expect more feedback from teachers and greater interaction with others.

6. Conclusion and future work

Our findings show that the network interaction scale of some online courses expanded during the COVID-19 pandemic. The network scale of most courses did not change significantly, demonstrating that the pandemic did not notably alter the scale of course interaction. Online learning interaction among course network members whose interaction scale increased also became more frequent during the pandemic. Once the outbreak was under control, although the scale of interaction declined, the level and scope of some courses’ interactive networks continued to rise; interaction was thus particularly effective in these cases. Overall, the pandemic appeared to have a relatively positive impact on online learning interaction. We considered a pair of courses in detail and found that Economics (a social science course) fared much better than Electrodynamics (a natural science course) in classroom interaction; learners were more willing to partake in-class activities, perhaps due to these courses’ unique characteristics. Brint et al. also came to similar conclusions [ 57 ].

This study was intended to be rigorous. Even so, several constraints can be addressed in future work. The first limitation involves our sample: we focused on a select set of courses hosted on China’s icourse.163 MOOC platform. Future studies should involve an expansive collection of courses to provide a more holistic understanding of how the pandemic has influenced online interaction. Second, we only explored the interactive relationship between learners and did not analyze interactive content. More in-depth content analysis should be carried out in subsequent research. All in all, the emergence of COVID-19 has provided a new path for online learning and has reshaped the distance learning landscape. To cope with associated challenges, educational practitioners will need to continue innovating in online instructional design, strengthen related pedagogy, optimize online learning conditions, and bolster teachers’ and students’ competence in online learning.

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Original research article, faculty’s and students’ perceptions of online learning during covid-19.

• 1 Department of English Language and Translation, Applied Science Private University, Amman, Jordan
• 2 Department of Political Science, Applied Science Private University, Amman, Jordan
• 3 Department of Self-Development Skills, Najran University, Najran, Saudi Arabia

COVID-19 pandemic has disrupted teaching in a vriety of institutions. It has tested the readiness of academic institutions to deal with such abrupt crisis. Online learning has become the main method of instruction during the pandemic in Jordan. After 4 months of online education, two online surveys were distributed to investigate faculty’s and Students’ perception of the learning process that took place over that period of time with no face to face education. In this regard, the study aimed to identify both faculty’s and students’ perceptions of online learning, utilizing two surveys one distributed to 50 faculty members and another 280 students were selected randomly to explore the effectiveness, challenges, and advantages of online education in Jordan. The analysis showed that the common online platforms in Jordan were Zoom, Microsoft Teams offering online interactive classes, and WhatsApp in communication with students outside the class. The study found that both faculty and students agreed that online education is useful during the current pandemic. At the same time, its efficacy is less effective than face-to-face learning and teaching. Faculty and students indicated that online learning challenges lie in adapting to online education, especially for deaf and hard of hearing students, lack of interaction and motivation, technical and Internet issues, data privacy, and security. They also agreed on the advantages of online learning. The benefits were mainly self-learning, low costs, convenience, and flexibility. Even though online learning works as a temporary alternative due to COVID-19, it could not substitute face-to-face learning. The study recommends that blended learning would help in providing a rigorous learning environment.

Introduction

COVID-19 was declared as a global pandemic in March 2020 ( WHO, 2020 ). It impacted all walks of life including education. It led to the closure of schools and universities. This closure put a considerable burden on the academic institution to cope with the unprecedented shift from traditional to online learning. The outbreak triggered new ways of teaching online. Most countries imposed restrictions, where the medium of education has shifted into either synchronous or asynchronous modes. The world has seen the most extensive educational systems disruption in history in more than 190 countries worldwide. The closure of the academic institutions has impacted up to 99% of the world the student population in the lower lower-middle-income ( The Economic Times, 2020 ). The outbreak of COVID-19 established partial or complete lockdown, where people are forced to stay home. The higher education institutions’ closure demands online learning, where the course material is taught. For instance, Jordan, an Arab country, has replaced face-to-face instruction with online learning platforms to control the outbreak’s spread. The government had imposed a national lockdown, which resulted in universities’ and schools’ closure.

Most global institutions opt to use synchronous and asynchronous online teaching methods: synchronous is where faculty and their students meet in a pre-scheduled time as a part of interactive learning classes, while the asynchronous method refers to the Faculty giving the course without interaction with the students. There is no interaction between the faculty and students. Asynchronous modes of online learning suit students to access online material whenever they like ( EasyLMS, 2021 ). Faculty are the role players in making learning enjoyable, shaping students’ attitudes and personalities, and helping students pass. COVID-19 spreads online learning culture across the culture ( Beteille et al., 2020 ). COVID-19 forced the shift to online learning, but some universities in underdeveloped countries are not adequately equipped to teach online efficiently. Moreover, the faculty’s training is different globally between high-income, middle, and lower income countries. Another major obstacle is the Internet connectivity for underprivileged students. It is a de facto that face-to-face instruction is more efficient than online and the complete shift to online during COVID-19 makes it necessary to investigate the perception of faculty and students on online learning to identify the advantages and disadvantages, and challenges of online learning.

While the whole world is facing much trouble in the last few months, it has been difficult for the world, and the impact of online learning has been significantly observed on faculty members and students in particular. Teaching and learning online has a wide range of advantages, yet poses some challenges. It makes the process of learning for students’ comfort due to time flexibility in attending classes. However, online learning acts as a barrier to the engagement of students in real class activities. Moreover, students lack the influence of peer learning. These challenges also leave an impact on student’s personalities and prevent them from taking their turns. Additionally, the faculty’s role is to teach, monitor, and provide advice for students on both academic and personal levels. The current crisis, COVID-19, highlights the role of the Internet and technology in all walks of life including education. The pandemic has shown the role of online education in coping with abrupt crises, and therefore it is significant to understand both faculty’s and student’s perceptions concerning online classes.

Online Learning

There is a considerable development in education, where the mode of instruction has been changed from teacher-centered education to student-centered education. In teacher-centered education, the teacher plays a role as the source of education, and students are recipients of his/her knowledge. In contrast, student-centered education emphasizes the role of students in knowledge production in the class. In a student-centered approach, the teachers’ role turns to “helper to students who establish and enforce their own rules. Teachers respond to student assignments and encourage them to provide alternative/additional responses. Student-centered instruction has currently benefited many new technologies by using the internet and other advanced technological tools to share, transfer, and extend knowledge” ( Hancock, 2002 ). Online learning has become a part of the 21st century as it makes use of online platforms. E-learning is defined as using online platform technologies and the Internet to enhance learning and provide users with access to online services and services ( Ehlers and Pawlowski, 2006 ).

Internet and education have integrated to provide users with the necessary skills in the future ( Haider and Al-Salman, 2020 ). A study by Stec et al., 2020 indicated that online teaching has three main approaches, namely, enhanced, blended learning, and online approach. Enhanced learning uses the intensive use of technology to ensure innovative and interactive instruction. Blended learning mixes both face-to-face and online education. The online approach indicates that the course content is delivered online. Online education is convenient for students, where they can access online materials for 24 h ( Stern, 2020 ). Online education turns education to be student-centered, where students take part in the learning process, and teachers work as supervisors and guides for students ( Al-Salman et al., 2021 ).

Online platforms have different tools to facilitate conducting online interactive classes to reduce students’ loss. Online education platforms are designed to share information and coordinate class activities ( Martín-Blas and Serrano-Fernández, 2009 ). There are most famous prominent interactive online tools: DingTalk (interactive online platform designed by Alibaba Group), Hangouts Meet (video calls tool), Teams (chat, interactive meetings, video, and audio calls), Skype (video and audio calls), WeChat Work (video sharing and calls designed for the Chinese), WhatsApp (video and audio calls, chat, and content share), and Zoom (video and audio calls, and collaboration features) ( UNESCO, 2020 ).

Online Learning Before COVID-19 in the Arab Region

Online learning works as an alternative for face-to-face education during COVID-19. It becomes the 21st efficient tool for online learning. The online learning experience is different globally. Some countries have the required resources to facilitate learning, while many others do not have the equipment available in high and middle-income countries. In the Arab region, some countries such as Jordan, KSA, Qatar, Emirates, Bahrain, and Kuwait are relatively developed compared to other Arab countries. During COVID-19, most Arab higher education institutions shifted to synchronous and asynchronous online learning methods. Jordan, an Arab country, initiated online learning in the Ministry of Education and Ministries of Planning and Information Technology in 2002 ( Dirani and Yoon, 2009 ). They worked to start the online experience by shifting instruction mode from traditional to virtual. In a similar vein, Talal Abu-Ghazaleh University launched the first online platform to facilitate recruiting and enrolling new students and conducting virtual classes in 2012.

Moreover, Jordan’s university established synchronous blended learning, where some theoretical courses are conducted online, while practical times are campus oriented. Jordan was one of the countries to respond to the crisis in creating an online platform, Darsak, to facilitate online learning for schools ( Audah et al., 2020 ). However, online learning was not considered as an education modality in Jordan before this crisis.

Online Learning During COVID-19

COVID-19 was classified by world health organization (WHO) as a pandemic disease on March 11, 2020. On March 19, emergency state was declared as a response to prevent the spread of COVID-19. It is followed by a curfew, which lasted for 2 months. The mode of education has turned online due to the closure of universities. The closure of universities brings the importance of having good infrastructure and the readiness to conduct online classes. Jordan is considered as one of the leading countries in Internet infrastructure and has a highly developed Middle East region ( Jordan Times, 2017 ). Online learning becomes a tool to prevent the outbreak and ensure social distancing. Online education has useful learning tools and grants 24/7 access to education platforms around the clock at their time preferences. It also offers flexibility, regardless of place and time. It also gives students questions, answers freely, and provides feedback on the assigned courses’ content ( Rosell, 2020 ).

Literature and Research Questions

The Author’s literature review has uncovered that the faculty and students shall verify online learning’s importance during COVID-19. Therefore, the present study aims to bridge the gap by scrutinizing faculty and students’ perceptions of online learning. To be specific, it raises the following questions:

1. What is the opinion and perceptions of the faculty in terms of;

a. Online platform used and teaching experience.

b. Attitudes of computer literacy and online class preparations.

c. Attitude of the effectiveness of online education.

2. What is the student’s opinion and perceptions on the Effectiveness of online teaching & learning during covid-19 pandemic?

3. What are the challenges of online teaching & learning during the covid-19 pandemic?

4. What are the advantages, challenges, and disadvantages of online learning?

Literature Review

Technology has a firm-established role in education experience in the last decade ( Almahasees and Jaccomard, 2020 ). Methods, techniques, and strategies of education have been revised to deal with dramatic changes in technology. The technological enterprises have designed several online platforms, which are driven by the integration of technology in all walks of life ( Al-Azawei et al., 2017 ; Englund et al., 2017 ; Santos et al., 2019 ). Technology has become part of our social, business, and educational life’. The use of the Internet has a vital role in disseminating knowledge via online classes ( Silva and Cartwright, 2017 ).

During COVID-19, education has been shifted into the techno-economic culture. The shift should associate with plans to reduce this shift’s impact on the normal learning process ( Gurukkal, 2020 ). The change to online in higher education entails reshaping our view regarding higher education, including institutions and students’ needs. For instance, theoretical courses can be taught online. In contrast, the practical courses should be conducted face to face to ensure best teaching practices in monitoring and guiding students. Therefore, technology can make larger classes flexible and suiting students’ needs ( Siripongdee et al., 2020 ).

Research on faculty members’ perceptions and attitudes toward online learning emphasized the role of instructors in facilitating communication and earning with students. Instructors acknowledged the content expertise and instructional design as the factors in the success of online learning. Similarly, the call for staff and student training is mandatory for online learning success ( Cheng and Chau, 2016 ).

The mode of education has turned into student-centered education, where students became independent learners. This is considered as an advantage as face-to-face instruction was teacher-centered education, where students receive their education from their instructors. Online learning initiated students’ role in using additional resources to discover their abilities as independent learners ( Roach and Lemasters, 2006 ). The comparison between students’ attitudes toward teaching the same interactive courses in online and face to face is similar. It is found that students performed equally at the same interactive courses in online and face-to-face instruction. Face-to-face instruction’s success depends on regular class attendance, while the interactive classes relied on completing interactive worksheets. Therefore, online and face-to-face success is based on curriculum structure, mode of delivery, and completion rate ( Nemetz et al., 2017 ). The COVID-19 outbreak shifts face-to-face education to online during the lockdown. This shift helps faculty integrate advanced technological skills in their teaching, which benefit students ( Isaeva et al., 2020 ).

Online learning has been considered a useful tool for learning, cost-effectiveness, flexibility, and the possibility of providing world-class education ( Jeffcoat Bartley and Golek, 2004 ; Gratton-Lavoie and Stanley, 2009 ; De La Varre et al., 2010 ). A study by Li and Lalani (2020) indicated that COVID-19 had brought change to the status of learning in the 21st century. The instruction mode has been changed at both schools and higher academic from face-to-face instruction to online instruction ( Strielkowski, 2020 ). However, this rapid change tests the capacity of institutions to cope with such crises. Many countries did not expect such a complete shift to be online, and therefore their working staff and students are not trained enough for this dramatic change.

Online learning works as a tool to overcome abrupt crises ( Ayebi-Arthur, 2017 ). Online learning is considered as an entertaining way to learn. It has a positive impact on both students and teachers alike. Both faculty and students have optimistic opinions about online classes ( Kulal and Nayak, 2020 ). Moreover, there is a positive correlation between students and faculty in their perception of teaching and learning ( Seok et al., 2010 ). Faculty and students of engineering specialties incurred that theoretical engineering subjects can be taught online, while teaching practical courses online are less effective and should be conducted at engineering labs ( Kinney et al., 2012 ). Similarly, students’ and faculty perceptions were marginalized differently in teaching laboratory courses online ( Beck and Blumer, 2016 ).

Faculty and students encountered challenges such as technology, workload, digital competence, and compatibility. They concluded that education would become hybrid, face-to-face, and online instructions ( Adedoyin and Soykan, 2020 ). A study to verify the usage of online learning platforms in teaching clinical medical courses was conducted. They found that the rate of student satisfaction is 26% ( Al-Balas et al., 2020 ). There is a slew of advantages and disadvantages of online learning. The benefits include efficiency, cost-effectiveness, and 24 h access, while the disadvantages are technical issues, lack of interaction, and training ( Gautam, 2020 ). Rayan, 2020 proposed ways to overcome the disadvantages of online learning by encouraging shy students to participate and provoke students’ online class attendance. Understanding such issues will help to deliver adequate online education. Online encourages shy students to participate and improve students’ attendance, while it also triggers a lack of social interaction that affects students.

Online learning has a vital role in learning during the crisis. Moreover, having properly maintained the technical infrastructure is required for its success at schools and universities ( Nikdel Teymori and Fardin, 2020 ). Dhawan, 2020 scrutinizes online learning’s strengths, weaknesses, opportunities, and threats (SWOT). He shows that crisis highlights the role of technology competency in dealing with the global crisis and facilitating learning. Therefore, schools should train students with the necessary IT skills. Another study was conducted on male and female students’ satisfaction in using E-learning portals in Malaysia. He found that there is a significant relationship between the user’s satisfaction and E-learning. The satisfaction rate by both participants depends on E-service quality and the information provided ( Shahzad et al., 2020 ). The advantages of online learning are as follows: flexibility, easy access, and interaction between learners and their professors ( Strayer University, 2020 ). The role and advantages of online learning have accentuated that online learning has challenges as data privacy. Students’ private information is at risk since they use their computers and mobile phones to access online portals. Universities should educate their staff and students about cybersecurity and data privacy ( Luxatia, 2020 ).

Methodology

Participants.

The population of the study was instructors and students at both undergraduate and postgraduate levels. Fifty faculty members and 280 students were selected randomly from this population, which is deemed significant to provide useful feedback on both faculty’s and students’ perceptions of online learning.

The study used two online surveys, which is delivered to participants in the period between September 15 and November 15 during the closure of universities in Jordan to control the spread of COVID-19. The online two surveys were created Google Forms and sent to the faculty and students through emails, Facebook Messenger, WhatsApp messages, and LinkedIn to have social distancing. Thirty-four male and fifteen female members of Faculty participated in the survey.

Thirty-eight participants hold Ph.D. and 11 master’s degrees. The mean of faculty ranges from 31 to 50 years old with an standard deviation (SD) of 1.00224. 47 members of the Faculty teach at university, while three members of the Faculty teach at college. Seven of the participants were professors, 11 associate professors, 18 assistant professors, nine lecturers, and four teaching assistants.

A total of 280 were undergraduate students. Eighty-eight were males, and 192 were females. As for the major, 175 were studying theoretical majors and 105 were studying in practical disciplines, 237 of them live in urban areas, and 43 live in rural areas. Of these, 151 were using mobile to access online classes and 106 were using laptops, while 25 of the students were using a tablet. One-hundred and forty-nine of the study samples indicated that they had received training in using the online classes, while 131 had received no training.

Data Gathering Instruments

Two online surveys were created by Google Docs. The faculty survey consisted of three parts such as sociodemographic, online education training, and faculty’s perceptions of teaching online effectiveness. On the other hand, the students’ survey consisted of four parts, namely, sociodemographic, students’ perception of online learning’s effectiveness, advantages, and challenges of online learning. The survey was designed in a Likert Scale format for rating statements. Two professors reviewed the two surveys, and proper changes were made before disseminating the two surveys of the participants. Participation in the study was voluntary, and personal information was not gathered. Data were imported into Excel to facilitate SPSS analysis using 25 versions.

Validity and Reliability

Two experts examined the two surveys cross out to validate the survey’s design. Their comments are taken into account of omitting some items of the survey due to their irrelevance. As for reliability, Cronbach’s alpha was used as a measure of internal consistency to indicate how the items are closely related. The result of the test showed that the items of the two surveys are consistent. For the faculty survey, the alpha coefficient for the 26 items is 0.889 for the faculty’s survey and 0.896 for the students’ survey, suggesting that the items have relatively high internal consistency. A reliability coefficient of 0.70 or higher is considered “acceptable” in most social science research situations ( Mockovak, 2016 ).

The findings are structured according to sections of the surveys.

Faculty’s Survey

Online teaching experience.

First, the current study scrutinizes the readiness of instructors to teach online. The analysis showed that most of the faculty had previous experience of teaching online before COVID-19, with a percentage of 60%. In contrast, 40% of the surveyed faculty did not have experience in teaching online before COVID-19. Those who had previous experience showed that they had received training to teach online with a percentage of 66%, while 34% did not have any activity to do online learning sessions.

The faculty showed that they used Zoom and Microsoft Teams in their online teaching with 60% for Microsoft Teams and 40% for Zoom. Finally, most participants uncovered that they used WhatsApp with 70% as a medium of communication between the tutor and his students outside the online class time. The second popular platform is Zoom and Microsoft Teams chat and text options with 28%. Moreover, Facebook pages occupied the third rank with 14%, while phone calls were used by 8% of the participants (see Table 1 ).

Table 1. Common online platforms and teaching experiences.

Faculty’s Attitudes of Computer Literacy and Online Class Preparations

The second division of the survey was to identify computer literacy and online class preparation to indicate computer and IT skills as shown in Table 2 . The majority of the respondents agreed that they have enough IT skills to conduct online classes. Moreover, online courses require more effort to do online courses in comparison to face-to-face instruction. Online learning becomes a tool to cope with all catalyst times such as COVID-19.

Table 2. Attitude of IT skills and online class preparations.

Faculty’s Attitude Toward the Effectiveness of Online Education

The third part of the survey was on the faculty’s attitude toward the effectiveness of online education. The faculty’s responses on the possibility of taking online courses without direct contact between the faculty and their students were centered on neutralism value, which was reflected in the mean scores of the instructors’ responses ( M = 3.1224, SD = 1.37890, p < 0.001). The faculty’s perception was also neutral in the second, third, fourth, and fifth items. The remaining items received agreement value except for the seventh item, which was between neutralism and agreement. These values were statistically significant after Bonferroni was corrected ( p < 0.001) (see Table 3 ).

Table 3. Faculty’s perception of the effectiveness of online teaching.

Students’ Survey

The effectveness of online teaching and learning during the covid-19 pandemic.

First, the study examined the effectiveness of online learning during COVID-19 (see Table 4 ). The effectiveness of online learning ranges in delivering online learning during the crisis with an SD of 0.67 and 3.548. This means the study participants found online learning useful due to the following reasons: first, students showed that they were provided with efficient online platforms by their institutions to attend lectures. The majority of the study’s respondents showed that they used Microsoft Teams in their online learning process. This is affirmed by Spataro (2020) that Microsoft Teams, as of the end of October 2020, has increased significantly to reach 115 million daily active users. Second, the study’s participants showed that they were trained and had the necessary technological skills to attend online learning. Trained students on online platforms could grasp the learning outcomes of online classes.

Table 4. The perception of online teaching & learning during the covid-19 pandemic.

Moreover, they also showed that they gained new experiences while attending online classes. Third, students emphasize that online learning platforms are easy to use. This means that students have got training to attend online classes, while the academic institutions may share guideline usage with their students. Furthermore, online learning allows flexible time to participate in courses whether they attend the classes synchronously (the exact time of the lecture) and asynchronously (recording the study). Fourth, students accentuated that they were satisfied with the student–teacher interaction during online teaching and learning.

Similarly, the participants showed their agreement on communications and asked questions to clear their doubts during online lectures. On the other hand, the study’s participants responded as neither agree nor disagree (NAND) to the question of whether students’ motivation is high in participating in online lectures. In the same vein, the study’s analysis indicated that they were not able to decide whether their home is suitable to attend online lectures. This means may the students may have got external distractions from their family members while attending online classes.

The research sample agrees on the effectiveness of learning using online classes with a mean of 3.548 (agree) and a standard deviation of 0.647. Most of these opportunities were: you have sufficient equipment and facilities with a mean of 4.09 and a standard deviation of 0.926, and you have adequate computer knowledge and IT skills to manage your online learning with a mean of 3.9321 and a standard deviation of 0.93845, and online tools are easy to use with a mean of 3.8929 and a standard deviation of 0.99242.

The Challenges of Online Teaching and Learning During the COVID-19 Pandemic

The students emphasized that they faced a set of challenges through online learning due to the abrupt shift from face-to-face instruction to online instructions (see Table 5 ). Students’ responses showed that they faced the following challenges. First, students faced a challenge in adapting themselves to online learning. They could have such problems due to technical issues such as the lack of IT competency. Second, students faced a challenge in having proper access to the Internet for many reasons, such as the cost of having a fiber network, which is not affordable for some students. The students also reported that they faced challenges in managing their time and organizing their homework to submit their tasks. Moreover, some of the students have shown that the lack of interaction is also considered a challenge for students, reflecting on their progress and personalities.

Table 5. The challenges of online teaching and learning during the COVID-19 pandemic.

Moreover, they added that adjusting online classes for students with special needs is a tremendous challenge for deaf, hard of hearing, or disabilities. Furthermore, the study’s respondents also indicated that online learning classes lack insufficient tools for student assessment. Moreover, online learning classes do not let instructors identify the individual differences between students quickly. More importantly, the study’s analysis showed that students were concerned about their data privacy since using their laptops or mobile phones at home, which exposes their data for breach.

It is obvious from Table 5 that the research sample agrees with the learning challenges using online classes with a mean of 3.704 (agree) and a standard deviation of 0.600. The most important of these challenges came for adjusting online courses to deaf or hard of hearing five students and students with disabilities with an average of 3.8143 and a standard deviation of 0.995. Moreover, technical and Internet issues occupied the second rank with a mean of 3.7857 and a standard deviation of 0.996, and the organization of work processes and time management with an average of 3.7036 and a standard deviation of 1.020.

The Advantages of Online Teaching and Learning During the COVID-19 Pandemic

Students opined that online learning ensures that the students will have access to the learning materials based on their convenient time if online learning classes are asynchronously recorded at any time in a day. Moreover, online learning encourages students to take part in the learning process since the instruction mode shifted to focus on student learning (self-paced learning). Students also expressed that online learning helped them to acquire new experiences and skills. It also reduced the cost of traveling to universities and related expenses. Use of traveling resources and other charges as shown in Table 6 .

Table 6. The advantages of online teaching and learning during the COVID-19 pandemic.

Table 3 indicates that the research sample agrees on the advantages of learning using online classes with an average of 3.673 (agree) and a standard deviation of 0.858. The most important of these features were in (self-paced learning) with a mean of 3.789 and a standard deviation of 0.970, and you can learn whatever you want with a mean of 3.778 and a standard deviation of 1.064, and comfort advantage with a mean of 3.707 and a standard deviation of 1.139.

An analysis of the faculty’s and students’ responses showed their perception of online learning during COVID-19. Faculty were surveyed in terms of online teaching experience, computer literacy, class preparation, and online learning effectiveness. On the other hand, students were studied in terms of online energy, challenges, and advantages. The significant results were interpreted and discussed below.

The analysis showed that 68% of the faculty members had undergone training, while 32% did not have. Exercise is part of development programs provided by universities to equip their staff with the necessary skills. This criterion highlights Faculty Academic Development Centers’ role to have plans to deal with all abrupt crises such as COVID-19. Training programs should not be limited to faculty; they should also involve students. The study found that Zoom and Microsoft Teams were used by the surveyed faculty more than others in conducting virtual classes. Moreover, WhatsApp is the most popular platform for communication between faculty and their students outside classrooms. WhatsApp has been used by more than 2 billion users monthly as of October 2020 ( Statista, 2020 ).

Faculty’s Perception of Computer Literacy and Online Class Preparations

The majority of respondents revealed that they had computer competency before the emergence of COVID-19. This competency helped the faculty to do online classes since IT skills are mandatory for the technology learning environment, as indicated by Li and Lee (2016) . However, the study showed that faculty preferred traditional teaching, face to face, more than online. Face-to-face instruction allows the ability to discuss and have lively guidance for your students. It encourages students’ engagement and reflects positively on the level of students ( Cooke, 2020 ). Therefore, most of the faculty members indicated that online classes’ preparation entails more effort to ensure having interactive online courses.

Faculty’s Perception of the Effectiveness of Online Teaching

The study showed that faculty agreed on the point of online learning To be concise, faculty responses were debatable whether students at online classes can outperform students with face-to-face instruction, as reflected in the item’s mean score ( M = 2.9388). However, the fact is that face-to-face students need the education to excel in online learning results in scores of faculty responses ( M = 3.8367). Faculty also showed that the lack of interaction between students and their instructors might lead to low performance. The faculty were asked if they were able to assess students fairly. The study’s results showed that faculty knew the individual differences between students in online classes. Moreover, online courses helped them to achieve the learning outcomes of their academic syllabi.

Faculty’s Perception of Time and Assignment Management

The analysis revealed that the faculty agreed to make their online sessions short. This finding showed that online classes should not keep the students’ attention and ensure their understanding. If the online course is long, the students may get bored and distracted. As for online class preparation, the participants agreed that online classes require more time than traditional classes. Of course, preparation for online courses entails a longer time than regular classes.

Regarding assignments, the faculty agreed that students should do more assignments in online learning than in traditional classes. Remote teaching requires students to do more tasks than conventional courses to ensure students’ effective practice. Besides, students’ assignments may compensate the students for the lack of direct contact with the tutors.

Online Learning Effectiveness and Challenges During COVID-19

This study highlights undergraduate students’ perceptions, which showed online learning as a flexible and useful learning source during the crisis and some limitations. According to students, online learning is a relaxed and productive source of knowledge. Most of them agreed that online learning helps students 24 h to have access to learning materials asynchronously at any time in a day. This finding correlates with ( Adedoyin and Soykan, 2020 ; Gautam, 2020 ) that online learning offers learners the ability to access online materials around the clock. Moreover, it also encouraged self-learning, where the student plays a role in the process of learning. Online learning reduces the cost of education, where students stay at home and do not pay any charge for traveling and other expenses. More importantly, students learned new experiences through learning, such as time management and self-discipline.

Student Challenges During COVID-19

The analysis revealed that the students faced difficulties when attending online classes. Based on the findings, these challenges lie in students’ struggle to adapt to online courses, lack of direct contact with the faculty, lack of motivation to attend classes, and time management. This list of challenges should be considered by course coordinators and program chairs by offering solutions to these challenges. Students viewed the issue of adapting to the transference from face to face to online instructions as a challenge. This is a great challenge since most countries were not prepared enough to cope with abrupt crises that we did not have before. Students also highlighted that online platforms are not easily adjustable to deaf, hard of hearing, or special needs students. The government should help such students by offering courses provided by specialists of students with special needs. Students also complained about the lack of interaction, reflecting on students’ achievements and their personalities. Technical Internet connectivity issues also affect learning via learning modalities. This challenge can be overcome by improving the speed of the Internet packages provided to students. In this context, governments should offer Internet packages to students at low cost, and the telecommunication companies should help students. Similarly, students were concerned about their data privacy since their information was exposed to breach by external parties, they use their laptops and PCs available at their homes. This requires that universities should educate students about data privacy. They also have to provide students with free firewall programs to protect their data, as also suggested by Luxatia (2020) .

The study scrutinized the perception of the faculty and students on online learning. The study showed that online education is less effective than online classes. The students of online learning face several challenges due to the struggle to complete adaptation to online courses and the lack of interaction between students and their tutors. E-learning platforms motivate student-centered learning, and they are easily adjustable during abrupt crises, such as COVID-19. The universities in Jordan should take part in training students on how to protect their data. Moreover, the government should advise telecommunication companies to improve the students’ services at an affordable price. It is worth mentioning that students with special needs should have synchronous classes, where the special needs specialists should have a role to facilitate such students’ process.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

KM and MA made substantial contributions to the conception, research questions, or design work, or the acquisition, analysis, or interpretation of data for the work, drafted and revised the work, and proofread the final version of the manuscript. All authors contributed to the article and approved the submitted version.

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.

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Keywords : perceptions of online learning, online learning, education during COVID-19, blended learning, online learning in Jordan, benefits and challenges of online learning

Citation: Almahasees Z, Mohsen K and Amin MO (2021) Faculty’s and Students’ Perceptions of Online Learning During COVID-19. Front. Educ. 6:638470. doi: 10.3389/feduc.2021.638470

Received: 06 December 2020; Accepted: 24 March 2021; Published: 12 May 2021.

Reviewed by:

Copyright © 2021 Almahasees, Mohsen and Amin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zakaryia Almahasees, [email protected]

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Main Article Content

Covid-19, education and access to digital technologies: a case study of a secondary school in gauteng, linda scott.

The first South African case of the coronavirus disease (COVID-19) in March 2020 escalated to the national lockdown by the end of March  2020. This caused serious repercussions for learners, as there is a lack of infrastructure in South Africa to support online teaching and  learning. The purpose of this study was to investigate the use of digital technologies during COVID-19 by teachers. A case study was used  and data were collected using a desktop literature review and a semi-structured, open-ended, qualitative questionnaire. The  questionnaire was administered to teachers at a secondary school in the Gauteng province, South Africa (SA) and Atlas.ti was used for  data analysis. We conclude that online learning was affected by socio-economic problems, the high cost of data and the lack of devices  and parental supervision. The recommendations include ways in which government could prepare for future crises that could arise, as  well as promoting ongoing attention to digital technology use in teaching and learning to address the digital divide in South Africa. With  this study we add to the body of knowledge regarding COVID-19 and the use of digital technologies in teaching and learning, which will  assist the government and teachers in understanding the problems and solutions for the use of digital technology in teaching and  learning. 

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Journal Identifiers

Systematic literature review: The adaptation of distance learning process during the COVID-19 pandemic using virtual educational spaces in metaverse

Affiliation.

• 1 Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia.
• PMID: 36643176
• PMCID: PMC9829423
• DOI: 10.1016/j.procs.2022.12.137

Virtual education has started to grab interest since technology emerges in everything in our life. Metaverse is one of the technologies that has been conceived since the late 90s and is currently being renewed and renovated to suit current changes. Yet, many teachers and students still do not know how to utilize Metaverse to create a new atmosphere in the learning process. Therefore, this study aims to show the results of using Metaverse in the form of virtual space in the educational field and how teachers and students respond to the process. In this study, the authors would like to conduct a Systematic Literature Review (SLR) on the advantages and disadvantages of virtual educational spaces in Metaverse based on many previous studies related to this topic. Survey results conducted by previous studies show that students mostly enjoy using Metaverse as their learning method and can comprehend several lessons better when using Metaverse than traditional learning- textbook-based and face-to-face learning. Unfortunately, only several studies focus on finding preferred subjects to teach using the Metaverse and for which educational level this method suits best. In general, the authors conclude that Metaverse has excellent potential in the future to be explored profoundly in the education field due to the development of skills in the use of technology and a significant increase in student practice scores. However, the guidance of teachers and parents is still needed so that students can avoid the disadvantages.

Keywords: Distance Learning; Education; Metaverse; Virtual Space.

© 2022 The Author(s). Published by Elsevier B.V.

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• v.36(COVID19-S4); 2020 May

Advantages, Limitations and Recommendations for online learning during COVID-19 pandemic era

Khadijah mukhtar.

1 Khadijah Mukhtar, BDS, MME. Assistant Professor, DME. University College of Medicine and Dentistry, The University of Lahore, Lahore, Pakistan

Kainat Javed

2 Kainat Javed, MBBS, MME. Assistant Professor, DME. University College of Medicine and Dentistry, The University of Lahore, Lahore, Pakistan

Mahwish Arooj

3 Mahwish Arooj, MBBS, M. Phil, MME, PhD Physiology. Associate Professor, Physiology and Director DME, University College of Medicine and Dentistry, The University of Lahore, Lahore, Pakistan

Ahsan Sethi

4 Ahsan Sethi, BDS, MPH, MMEd, FHEA, MAcadMEd, FDTFEd, PhD Medical Education Assistant Professor, Institute of Health Professions Education and Research, Khyber Medical University, Peshawar, Pakistan

During COVID-19 pandemic, the institutions in Pakistan have started online learning. This study explores the perception of teachers and students regarding its advantages, limitations and recommendations.

This qualitative case study was conducted from March to April 2020. Using maximum variation sampling, 12 faculty members and 12 students from University College of Medicine and University College of Dentistry, Lahore were invited to participate. Four focus group interviews, two each with the faculty and students of medicine and dentistry were carried out. Data were transcribed verbatim and thematically analyzed using Atlas Ti.

The advantages included remote learning, comfort, accessibility, while the limitations involved inefficiency and difficulty in maintaining academic integrity. The recommendations were to train faculty on using online modalities and developing lesson plan with reduced cognitive load and increased interactivities.

Conclusion:

The current study supports the use of online learning in medical and dental institutes, considering its various advantages. Online learning modalities encourage student-centered learning and they are easily manageable during this lockdown situation.

INTRODUCTION

The spread of COVID-19 has led to the closure of educational institutions all over the world. This tested the preparedness of universities to deal with a crisis that requires the help of advanced technology including hardware and software to enable effective online learning. Such closure accelerated the development of the online learning environments so that learning would not be disrupted. 1 Many institutions have become interested in how to best deliver course content online, engage learners and conduct assessments. Hence, COVID-19 while being a hazard to humanity, has evolved institutions to invest in online learning.

Online learning systems are web-based software for distributing, tracking, and managing courses over the Internet. 2 It involves the implementation of advancements in technology to direct, design and deliver the learning content, and to facilitate two-way communication between students and faculty. 3 They contain features such as whiteboards, chat rooms, polls, quizzes, discussion forums and surveys that allow instructors and students to communicate online and share course content side by side. These can offer productive and convenient ways to achieve learning goals. In Pakistan, the institutions are using Microsoft Teams, Google meet, Edmodo and Moodle as learning management systems along with their applications for video conferencing. 4 Other commonly used video conferencing solutions include Zoom, Skype for business, WebEx and Adobe connect etc.

According to our literature review, three previous studies were found, 5 - 7 supporting online learning from Pakistan. The two studies at Dow University of Health Sciences, Karachi and Lahore Medical and Dental College, Lahore reported high student satisfaction with online learning modalities. The study from Khyber Pakhtunkhwa assessed the feasibility of online learning among students, trainees and faculty members. They reported good technology access, online skills, and preparedness for online discussions among participants across the medical education continuum.

With the increase in use of online modalities during COVID-19, it is necessary to assess their effectiveness with regards to teaching and learning from various stakeholders. 8 Therefore, the current study explores the perception of faculty members and students regarding the advantages, limitations and recommendations for online learning in Pakistan. The study is timely as Higher Education Commission (HEC) is in the process of implementing online learning across all the universities in Pakistan. The findings will help identify the required changes on priority basis to make it more practical and worthwhile.

This qualitative case study was conducted from March to April 2020 in two medical and dental institutes. Ethical approval for this study was taken from ethical review board of University of Lahore (Ref No. ERC/02/20/02, dated February 25, 2020). Using maximum variation sampling 12 faculty members and 12 students from University College of Medicine and University College of Dentistry, Lahore were invited to participate. In addition to learning management system ‘Moodle’, these colleges have recently adopted ‘Zoom’ for interactive teaching in small and large group formats. The participants were also involved in online Problem-Based Learning sessions, along with regular online assessments during COVID-19 pandemic.

An interview guide was developed to explore faculty and students’ perception about online learning modalities, its advantages, limitations and recommendations. The interview guide was piloted to ensure comprehensiveness and then also validated by two medical education experts. 9 Two interviewers who were not involved in teaching and assessment of students conducted four focus group interviews (n=6 in each group) with faculty members (n=12) and students (n=12) of medicine and dentistry. The faculty and students were from both basic sciences (1 st and 2 nd year) and clinical sciences (3 rd , 4 th and final year). All interviews were recorded through ‘Zoom’ and subsequently transcribed verbatim. The data were thematically analyzed: compiling, disassembling, reassembling and interpretation by all the authors independently and then corroborated to ensure analytical triangulation.

The faculty members were predominantly females from both basic and clinical sciences with age range from 30-64 years. The students were from all professional years of MBBS and BDS program ( Table-I ).

Participant characteristics.

Total six themes, two each for advantages, limitations and recommendations were extracted from the transcribed data after qualitative analysis ( Table-II ).

E-learning advantages, limitations and recommendations by Students and Faculty.

Faculty opined that online learning helped ensure remote learning, it was manageable, and students could conveniently access teachers and teaching materials. It also reduced use of traveling resources and other expenses. It eased administrative tasks such as recording of lectures and marking attendance. Both the students and teachers had an opinion that online learning modalities had encouraged student-centeredness during this lockdown situation. The student had become self-directed learners and they learnt asynchronously at any time in a day.

Limitations

Faculty members and students said that through online learning modalities they were unable to teach and learn practical and clinical work. They could only teach and assess knowledge component. Due to lack of immediate feedback, teachers were unable to assess students’ understanding during online lecturing. The students also reported limited attention span and resource intensive nature of online learning as a limitation. Some teachers also mentioned that during online study, students misbehaved and tried to access online resources during assessments.

Recommendations

Teachers and students suggested continuous faculty development. They recommended a reduction in cognitive load and increased interactivities during online teaching. Those in clinical years suggested ways to start online Case Based Learning. However, some were also of the opinion that there should be revision classes along with psychomotor hands on teaching after the COVID-19 pandemic is under control. To enhance quality, they suggested buying premium software and other proctoring software to detect cheating and plagiarism.

The current study reported advantages, limitations and recommendations to improve online learning during lockdown of institutions due to COVID-19 pandemic. This study interprets perspectives of medical/dental students and faculty members, which showed that online learning modalities are flexible and effective source of teaching and learning along with some pitfalls. According to the teachers and students, online learning is a flexible and effective source of teaching and learning as most of them agreed upon the fact that this helps in distant learning with easy administration and accessibility along with less use resource and time. Regardless of time limit, students can easily access the learning material. This flexibility over face to face teaching has been reported in the literature as well. 2 The students also become self-directed learners, which is an important competency for encouraging lifelong learning among health professionals. 10 , 11

Both the faculty members and students viewed inefficiency to teach psychomotor skills, resource intensiveness and mismanaged decorum during sessions as limitations of online learning. Even though, hands-on sessions such as laboratory and clinical skills teaching have been disrupted during COVID-19 pandemic, we believe that online simulated patients or role plays can be used teach history taking, clinical reasoning and communication skills. Sharing recorded videos of laboratory and clinical skills demonstration is also worthwhile. Faculty members also complained about lack of students’ feedback regarding understanding of subject. Research showed that regular two-way feedback helps enhance self-efficacy and motivation. 12 The interaction between facilitator, learner and study material along with emotional and social support are essential ingredients for effective learning. 13 , 14 Internet connectivity issues also adversely impacted learning through online modalities, however, simply improving internet package/speed would help resolve this. Government should also take immediate measures and telecommunication companies should invest in expanding its 4G services across the country.

Recommendations reflect that decorum can be maintained by thorough supervision of students, setting ground rules for online interaction, counselling and disciplinary actions. 15 According to students, the attention span during online learning was even shorter than face to face sessions as also supported by the literature. 16 This can be managed by using flipped classroom learning modalities, giving shorter lectures and increasing teacher-student interaction. As ‘assessment drives learning’, so online formative assessments can be conducted through Socrative and Kahoot etc. Faculty needs training and students orientation in using online learning tools. 17 Investment in buying premium software packages will also help overcome many limitations and is therefore recommended.

Limitations of the Study

As the study participants belonged to the medical and dental college from a single private-sector university of Punjab, therefore the findings are only applicable to similar contexts. For generalizability, a survey based on our findings should be conducted across the province or country. Despite the limitations, the findings offer an understanding of the advantages, limitations and recommendations for improvement in online learning, which is the need of the day.

The current study supports the use of online learning in medical and dental institutes, considering its various advantages. E-learning modalities encourage student-centered learning and they are easily manageable during this lockdown situation. It is worth considering here that currently online learning is at a nascent stage in Pakistan. It started as ‘emergency remote learning’, and with further investments we can overcome any limitations. There is a need to train faculty on the use of online modalities and developing lesson plan with reduced cognitive load and increased interactivities.

Author’s Contribution

AS and MA conceived the idea , designed the study and are responsible for integrity of research.

KM and KJ collected the data.

All the authors contributed towards data analysis and writing the manuscript and approved the final version.

Acknowledgements

The authors would like to acknowledge the participants for their time and contributions.

Conflict of interest: None.

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