online learning during pandemic essay spm

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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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Distance learning in higher education during COVID-19: The role of basic psychological needs and intrinsic motivation for persistence and procrastination–a multi-country study

Roles Conceptualization, Methodology, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Developmental and Educational Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria

Roles Conceptualization, Data curation, Methodology, Project administration, Writing – review & editing

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

Roles Conceptualization, Methodology, Writing – review & editing

Roles Data curation, Writing – review & editing

Affiliation Department of Mathematics, Faculty of Mathematics, University of Vienna, Vienna, Austria

Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Roles Conceptualization, Funding acquisition, Methodology

Affiliation Department of Psychology, Faculty of Education, Aleksandër Moisiu University, Durrës, Albania

Affiliation Department of Educational Sciences, Faculty of Philology and Education, Bedër University, Tirana, Albania

Affiliation Xiangya School of Nursing, Central South University, Changsha, China

Affiliations Xiangya School of Nursing, Central South University, Changsha, China, Department of Nursing Science, University of Turku, Turku, Finland

Affiliation Study of Nursing, University of Applied Sciences Bjelovar, Bjelovar, Croatia

Affiliation Baltic Film, Media and Arts School, Tallinn University, Tallinn, Estonia

Affiliation Faculty of Educational Sciences, University of Helsinki, Helsinki, Finland

Affiliation Department of Psychology, University of Bonn, Bonn, Germany

Affiliation Chair of Educational Psychology, Technische Universität Berlin, Berlin, Germany

Affiliation Department of Educational Studies, University of Potsdam, Potsdam, Germany

Affiliation Faculty of Education, University of Akureyri, Akureyri, Iceland

Affiliation Department of Global Education, Tsuru University, Tsuru, Japan

Affiliation Career Center, Osaka University, Osaka University, Suita, Japan

Affiliation Graduate School of Education, Osaka Kyoiku University, Kashiwara, Japan

Affiliation Department of Psychology, Faculty of Philosophy, University of Prishtina ’Hasan Prishtina’, Pristina, Kosovo

Affiliation Department of Social Work, Faculty of Philosophy, University of Pristina ’Hasan Prishtina’, Pristina, Kosovo

Affiliation Department of Psychology, Faculty of Social Sciences and Humanities, Klaipėda University, Klaipėda, Lithuania

Affiliation Geography Department, Junior College, University of Malta, Msida, Malta

Affiliation Institute of Family Studies, Faculty of Philosophy, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia

Affiliation Institute of Psychology, Faculty of Social Science, University of Gdańsk, Gdańsk, Poland

Affiliation Faculty of Historical and Pedagogical Sciences, University of Wrocław, Wrocław, Poland

Affiliation Faculty of Educational Studies, Adam Mickiewicz University, Poznań, Poland

Affiliation CERNESIM Environmental Research Center, Alexandru Ioan Cuza University, Iași, România

Affiliation Social Sciences and Humanities Research Department, Institute for Interdisciplinary Research, Alexandru Ioan Cuza University of Iași, Iași, România

Affiliation Department of Informatics, Örebro University School of Business, Örebro University, Örebro, Sweden

Affiliation Faculty of Social Studies, Penn State University, State College, Pennsylvania, United States of America

•  [ ... ],

Affiliations Department of Developmental and Educational Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria, Department for Teacher Education, Centre for Teacher Education, University of Vienna, Vienna, Austria

• [ view all ]
• [ view less ]
• Elisabeth R. Pelikan, 
• Selma Korlat, 
• Julia Reiter, 
• Julia Holzer, 
• Martin Mayerhofer, 
• Barbara Schober, 
• Christiane Spiel, 
• Oriola Hamzallari, 
• Ana Uka, 

• Published: October 6, 2021
• https://doi.org/10.1371/journal.pone.0257346
• Peer Review
• Reader Comments

Due to the COVID-19 pandemic, higher educational institutions worldwide switched to emergency distance learning in early 2020. The less structured environment of distance learning forced students to regulate their learning and motivation more independently. According to self-determination theory (SDT), satisfaction of the three basic psychological needs for autonomy, competence and social relatedness affects intrinsic motivation, which in turn relates to more active or passive learning behavior. As the social context plays a major role for basic need satisfaction, distance learning may impair basic need satisfaction and thus intrinsic motivation and learning behavior. The aim of this study was to investigate the relationship between basic need satisfaction and procrastination and persistence in the context of emergency distance learning during the COVID-19 pandemic in a cross-sectional study. We also investigated the mediating role of intrinsic motivation in this relationship. Furthermore, to test the universal importance of SDT for intrinsic motivation and learning behavior under these circumstances in different countries, we collected data in Europe, Asia and North America. A total of N = 15,462 participants from Albania, Austria, China, Croatia, Estonia, Finland, Germany, Iceland, Japan, Kosovo, Lithuania, Poland, Malta, North Macedonia, Romania, Sweden, and the US answered questions regarding perceived competence, autonomy, social relatedness, intrinsic motivation, procrastination, persistence, and sociodemographic background. Our results support SDT’s claim of universality regarding the relation between basic psychological need fulfilment, intrinsic motivation, procrastination, and persistence. However, whereas perceived competence had the highest direct effect on procrastination and persistence, social relatedness was mainly influential via intrinsic motivation.

Citation: Pelikan ER, Korlat S, Reiter J, Holzer J, Mayerhofer M, Schober B, et al. (2021) Distance learning in higher education during COVID-19: The role of basic psychological needs and intrinsic motivation for persistence and procrastination–a multi-country study. PLoS ONE 16(10): e0257346. https://doi.org/10.1371/journal.pone.0257346

Editor: Shah Md Atiqul Haq, Shahjalal University of Science and Technology, BANGLADESH

Received: March 30, 2021; Accepted: August 29, 2021; Published: October 6, 2021

Copyright: © 2021 Pelikan 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: Data is now publicly available: Pelikan ER, Korlat S, Reiter J, Lüftenegger M. Distance Learning in Higher Education During COVID-19: Basic Psychological Needs and Intrinsic Motivation 2021. doi: 10.17605/OSF.IO/8CZX3 .

Funding: This work was funded by the Vienna Science and Technology Fund (WWTF) [ https://www.wwtf.at/ ] and the MEGA Bildungsstiftung [ https://www.megabildung.at/ ] through project COV20-025, as well as the Academy of Finland [ https://www.aka.fi ] through project 308351, 336138, and 345117. BS is the grant recipient of COV20-025. KSA is the grant recipient of 308351, 336138, and 345117. Open access funding was provided by University of Vienna. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

Introduction

In early 2020, countries across the world faced rising COVID-19 infection rates, and various physical and social distancing measures to contain the spread of the virus were adopted, including curfews and closures of businesses, schools, and universities. By the end of April 2020, roughly 1.3 billion learners were affected by the closure of educational institutions [ 1 ]. At universities, instruction was urgently switched to distance learning, bearing challenges for all actors involved, particularly for students [ 2 ]. Moreover, since distance teaching requires ample preparation time and situation-specific didactic adaptation to be successful, previously established concepts for and research findings on distance learning cannot be applied undifferentiated to the emergency distance learning situation at hand [ 3 ].

Generally, it has been shown that the less structured learning environment in distance learning requires students to regulate their learning and motivation more independently [ 4 ]. In distance learning in particular, high intrinsic motivation has proven to be decisive for learning success, whereas low intrinsic motivation may lead to maladaptive behavior like procrastination (delaying an intended course of action despite negative consequences) [ 5 , 6 ]. According to self-determination theory (SDT), satisfaction of the three basic psychological needs for autonomy, competence and social relatedness leads to higher intrinsic motivation [ 7 ], which in turn promotes adaptive patterns of learning behavior. On the other hand, dissatisfaction of these basic psychological needs can detrimentally affect intrinsic motivation. According to SDT, satisfaction of the basic psychological needs occurs in interaction with the social environment. The context in which learning takes place as well as the support of social interactions it encompasses play a major role for basic need satisfaction [ 7 , 8 ]. Distance learning, particularly when it occurs simultaneously with other physical and social distancing measures, may impair basic need satisfaction and, in consequence, intrinsic motivation and learning behavior.

The aim of this study was to investigate the relationship between basic need satisfaction and two important learning behaviors—procrastination (as a consequence of low or absent intrinsic motivation) and persistence (as the volitional implementation of motivation)—in the context of emergency distance learning during the COVID-19 pandemic. In line with SDT [ 7 ] and previous studies (e.g., [ 9 ]), we also investigated the mediating role of intrinsic motivation in this relationship. Furthermore, to test the universal importance of SDT for intrinsic motivation and learning behavior under these specific circumstances, we collected data in 17 countries in Europe, Asia, and North America.

The fundamental role of basic psychological needs for intrinsic motivation and learning behavior

SDT [ 7 ] provides a broad framework for understanding human motivation, proposing that the three basic psychological needs for autonomy, competence, and social relatedness must be satisfied for optimal functioning and intrinsic motivation. The need for autonomy refers to an internal perceived locus of control and a sense of agency. In an academic context, students who learn autonomously feel that they have an active choice in shaping their learning process. The need for competence refers to the feeling of being effective in one’s actions. In addition, students who perceive themselves as competent feel that they can successfully meet challenges and accomplish the tasks they are given. Finally, the need for social relatedness refers to feeling connected to and accepted by others. SDT proposes that the satisfaction of each of these three basic needs uniquely contributes to intrinsic motivation, a claim that has been proved in numerous studies and in various learning contexts. For example, Martinek and colleagues [ 10 ] found that autonomy satisfaction was positively whereas autonomy frustration was negatively related to intrinsic motivation in a sample of university students during COVID-19. The same held true for competence satisfaction and dissatisfaction. A recent study compared secondary school students who perceived themselves as highly competent in dealing with their school-related tasks during pandemic-induced distance learning to those who perceived themselves as low in competence [ 11 ]. Students with high perceived competence not only reported higher intrinsic motivation but also implemented more self-regulated learning strategies (such as goal setting, planning, time management and metacognitive strategies) and procrastinated less than students who perceived themselves as low in competence. Of the three basic psychological needs, the findings on the influence of social relatedness on intrinsic motivation have been most ambiguous. While in some studies, social relatedness enhanced intrinsic motivation (e.g., [ 12 ]), others could not establish a clear connection (e.g., [ 13 ]).

Intrinsic motivation, in turn, is regarded as particularly important for learning behavior and success (e.g., [ 6 , 14 ]). For example, students with higher intrinsic motivation tend to engage more in learning activities [ 9 , 15 ], show higher persistence [ 16 ] and procrastinate less [ 6 , 17 , 18 ]. Notably, intrinsic motivation is considered to be particularly important in distance learning, where students have to regulate their learning themselves. Distance-learning students not only have to consciously decide to engage in learning behavior but also persist despite manifold distractions and less external regulation [ 4 ].

Previous research also indicates that the satisfaction of each basic need uniquely contributes to the regulation of learning behavior [ 19 ]. Indeed, studies have shown a positive relationship between persistence and the three basic needs (autonomy [ 20 ]; competence [ 21 ]; social relatedness [ 22 ]). Furthermore, all three basic psychological needs have been found to be related to procrastination. In previous research with undergraduate students, autonomy-supportive teaching behavior was positively related to satisfaction of the needs for autonomy and competence, both of which led to less procrastination [ 23 ]. A qualitative study by Klingsieck and colleagues [ 18 ] supports the findings of previous studies on the relations of perceived competence and autonomy with procrastination, but additionally suggests a lack of social relatedness as a contributing factor to procrastination. Haghbin and colleagues [ 24 ] likewise found that people with low perceived competence avoided challenging tasks and procrastinated.

SDT has been applied in research across various contexts, including work (e.g., [ 25 ]), health (e.g., [ 26 ]), everyday life (e.g., [ 27 ]) and education (e.g., [ 15 , 28 ]). Moreover, the pivotal role of the three basic psychological needs for learning outcomes and functioning has been shown across multiple countries, including collectivistic as well as individualistic cultures (e.g., [ 29 , 30 ]), leading to the conclusion that satisfaction of the three basic needs is a fundamental and universal determinant of human motivation and consequently learning success [ 31 ].

Self-determination theory in a distance learning setting during COVID-19

As Chen and Jang [ 28 ] observed, SDT lends itself particularly well to investigating distance learning, as the three basic needs for autonomy, competence and social relatedness all relate to important aspects of distance learning. For example, distance learning usually offers students greater freedom in deciding where and when they want to learn [ 32 ]. This may provide students with a sense of agency over their learning, leading to increased perceived autonomy. At the same time, it requires students to regulate their motivation and learning more independently [ 4 ]. In the unique context of distance learning during COVID-19, it should be noted that students could not choose whether and to what extent to engage in distance learning, but had to comply with external stipulations, which in turn may have had a negative effect on perceived autonomy. Furthermore, distance learning may also influence perceived competence, as this is in part developed by receiving explicit or implicit feedback from teachers and peers [ 33 ]. Implicit feedback in particular may be harder to receive in a distance learning setting, where informal discussions and social cues are largely absent. The lack of face-to-face contact may also impede social relatedness between students and their peers as well as students and their teachers. Well-established communication practices are crucial for distance learning success (see [ 34 ] for an overview). However, providing a nurturing social context requires additional effort and guidance from teachers, which in turn necessitates sufficient skills and preparation on their part [ 34 , 35 ]. Moreover, the sudden switch to distance learning due to COVID-19 did not leave teachers and students time to gradually adjust to the new learning situation [ 36 ]. As intrinsic motivation is considered particularly relevant in the context of distance education [ 28 , 37 ], applying the SDT framework to the novel situation of pandemic-induced distance learning may lead to important insights that allow for informed recommendations for teachers and educational institutions about how to proceed in the context of continued distance teaching and learning.

In summary, the COVID-19 situation is a completely new environment, and basic need satisfaction during learning under pandemic-induced conditions has not been explored before. Considering that closures of educational institutions have affected billions of students worldwide and have been strongly debated in some countries, it seems particularly relevant to gain insights into which factors consistently influence conducive or maladaptive learning behavior in these circumstances in a wide range of countries and contextual settings.

Therefore, the overall goal of this study is to investigate the well-established relationship between the three basic needs for autonomy, competence, and social relatedness with intrinsic motivation in the new and specific situation of pandemic-induced distance learning. Firstly, we examine the relationship between each of the basic needs with intrinsic motivation. We expect that perceived satisfaction of the basic needs for autonomy (H1a), competence (H1b) and social relatedness (H1c) would be positively related to intrinsic motivation. In our second research question, we furthermore extend SDT’s predictions regarding two important aspects of learning behavior–procrastination (as a consequence of low or absent intrinsic motivation) and persistence (as the implementation of the volitional part of motivation) and hypothesize that each basic need will be positively related to persistence and negatively related to procrastination, both directly (procrastination: H2a –c; persistence: H3a –c) and mediated by intrinsic motivation (procrastination: H4a –c; persistence: H5a –c). We also proposed that perceived autonomy, competence, and social relatedness would have a direct negative relation with procrastination (H6a –c) and a direct positive relation with persistence (H7a –c). Finally, we investigate SDT’s claim of universality, and assume that the aforementioned relationships will emerge across countries we therefore expect a similar pattern of results in all observed countries (H8a –c). As previous studies have indicated that gender [ 4 , 17 , 38 ] and age [ 39 , 40 ]. May influence intrinsic motivation, persistence, and procrastination, we included participants’ gender and age as control variables.

Study design

Due to the circumstances, we opted for a cross-sectional study design across multiple countries, conducted as an online survey. We decided for an online-design due to the pandemic-related restrictions on physical contact with potential survey participants as well as due to the potential to reach a larger audience. As we were interested in the current situation in schools than in long-term development, and we were particularly interested in a large-scale section of the population in multiple countries, we decided on a cross-sectional design. In addition, a multi-country design is particularly interesting in a pandemic setting: During this global health crisis, educational institutions in all countries face the same challenge (to provide distance learning in a way that allows students to succeed) but do so within different frameworks depending on the specific measures each country has implemented. This provides a unique basis for comparing the effects of need fulfillment on students’ learning behavior cross-nationally, thus testing the universality of SDT.

Sample and procedure

The study was carried out across 17 countries, with central coordination taking place in Austria. It was approved and supported by the Austrian Federal Ministry of Education, Science and Research and conducted online. International cooperation partners were recruited from previously established research networks (e.g., European Family Support Network [COST Action 18123]; Transnational Collaboration on Bullying, Migration and Integration at School Level [COST Action 18115]; International Panel on Social), resulting in data collection in 16 countries (Albania, China, Croatia, Estonia, Finland, Germany, Iceland, Japan, Kosovo, Lithuania, Poland, Malta, North Macedonia, Romania, Sweden, USA) in addition to Austria. Data collection was carried out between April and August 2020. During this period, all participating countries were in some degree of pandemic-induced lockdown, which resulted in universities temporarily switching to distance learning. The online questionnaires were distributed among university students via online surveys by the research groups in each respective country. No restrictions were placed on participation other than being enrolled at a university in the sampling country. Participants were informed about the goals of the study, expected time it would take to fill out the questionnaire, voluntariness of participation and anonymity of the acquired data. All research partners ensured that all ethical and legal requirements related to data collection in their country context were met.

Only data from students who gave their written consent to participate, had reached the age of majority (18 or older) and filled out all questions regarding the study’s main variables were included in the analyses (for details on data cleaning rules and exclusion criteria, see [ 41 ]). Additional information on data collection in the various countries is provided in S1 Table in S1 File .

The overall sample of N = 15,462 students was predominantly female (71.7%, 27.4% male and 0.7% diverse) and ranged from 18 to 71 years, with the average participant age being 24.41 years ( SD = 6.93, Mdn = 22.00). Sample descriptives per country are presented in Table 1 .

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https://doi.org/10.1371/journal.pone.0257346.t001

The variables analyzed here were part of a more extensive questionnaire; the complete questionnaire, as well as the analysis code and the data set, can be found at OSF [ 42 ] In order to take the unique situation into account, existing scales were adapted to the current pandemic context (e.g., adding “In the current home-learning situation …”), and supplemented with a small number of newly developed items. Subsequently, the survey was revised based on expert judgements from our research group and piloted with cognitive interview testing. The items were sent to the research partners in English and translated separately by each respective research team either using the translation-back-translation method or by at least two native-speaking experts. Subsequently, any differences were discussed, and a consolidated version was established.

To assure the reliability of the scales, we analyzed them using alpha coefficients separately for each country (see S2–S18 Tables in S1 File ). All items were answered on a rating scale from 1 (= strongly agree) to 5 (= strongly disagree) and students were instructed to answer with regard to the current situation (distance learning during the COVID-19 lockdown). Analyses were conducted with recoded items so that higher values reflected higher agreement with the statements.

Perceived autonomy was measured with two newly constructed items (“Currently, I can define my own areas of focus in my studies” and “Currently, I can perform tasks in the way that best suits me”; average α = .78, ranging from .62 to .86).

Perceived competence was measured with three items, which were constructed based on the Work-related Basic Need Satisfaction Scale (W-BNS; [ 25 ]) and transferred to the learning context (“Currently, I am dealing well with the demands of my studies”, “Currently, I have no doubts about whether I am capable of doing well in my studies” and “Currently, I am managing to make progress in studying for university”; average α = .83, ranging from .74 to .91).

Perceived social relatedness was assessed with three items, based on the W-BNS [ 43 ], (“Currently, I feel connected with my fellow students”, “Currently, I feel supported by my fellow students”) and the German Basic Psychological Need Satisfaction and Frustration Scale [ 44 ]; “Currently, I feel connected with the people who are important to me (family, friends)”; average α = .73, ranging from .64 to .88).

Intrinsic motivation was measured with three items which were slightly adapted from the Scales for the Measurement of Motivational Regulation for Learning in University Students (SMR-LS; [ 45 ]; “Currently, doing work for university is really fun”, “Currently, I am really enjoying studying and doing work for university” and “Currently, I find studying for university really exciting”; average α = .91, ranging from .83 to .94).

Procrastination was measured with three items adapted from the Procrastination Questionnaire for Students (Prokrastinationsfragebogen für Studierende; PFS; [ 46 ]): “In the current home-learning situation, I postpone tasks until the last minute”, “In the current home-learning situation, I often do not manage to start a task when I set out to do so”, and “In the current home-learning situation, I only start working on a task when I really need to”; average α = .88, ranging from .74 to .91).

Persistence was measured with three items adapted from the EPOCH measure [ 47 ]: “In the current home-learning situation, I finish whatever task I begin”, “In the current home-learning situation, I keep at my tasks until I am done with them” and “In the current home-learning situation, once I make a plan to study, I stick to it”; average α = .81, ranging from .74 to .88).

Data analysis.

Data analyses were conducted using IBM SPSS version 26.0 and Mplus version 8.4. First, we tested for measurement invariance between countries prior to any substantial analyses. We conducted a multigroup confirmatory factor analysis (CFAs) for all scales individually to test for configural, metric, and scalar invariance [ 48 , 49 ] (see S19 Table in S1 File ). We used maximum likelihood parameter estimates with robust standard errors (MLR) to deal with the non-normality of the data. CFI and RMSEA were used as indicators for absolute goodness of model fit. In line with Hu and Bentler [ 50 ], the following cutoff scores were considered to reflect excellent and adequate fit to the data, respectively: (a) CFI > 0.95 and CFI > 0.90; (b) RMSEA < .06 and RMSEA < .08. Relative model fit was assessed by comparing BICs of the nested models, with smaller BIC values indicating a better trade-off between model fit and model complexity [ 51 ]. Configural invariance indicates a factor structure that is universally applicable to all subgroups in the analysis, metric invariance implies that participants across all groups attribute the same meaning to the latent constructs measured, and scalar invariance indicates that participants across groups attribute the same meaning to the levels of the individual items [ 51 ]. Consequently, the extent to which the results can be interpreted depends on the level of measurement invariance that can be established.

For the main analyses, three latent multiple group mediation models were computed, each including one of the basic psychological needs as a predictor, intrinsic motivation as the mediator and procrastination and persistence as the outcomes. These three models served to test the hypothesis that perceived autonomy, competence and social relatedness are related to levels of procrastination and persistence, both directly and mediated through intrinsic motivation. We used bootstrapping in order to provide analyses robust to non-normal distribution variations, specifying 5,000 bootstrap iterations [ 52 ]. Results were estimated using the maximum likelihood (ML) method. Bias-corrected bootstrap confidence intervals are reported.

Finally, in an exploratory step, we investigated the international applicability of the direct and mediated effects. To this end, an additional set of latent mediation models was computed where the path estimates were fixed in order to create an average model across all countries. This was prompted by the consistent patterns of results across countries we observed in the multigroup analyses. Model fit indices of these average models were compared to those of the multigroup models in order to establish the similarity of path coefficients between countries.

Statistical prerequisites

Table 2 provides overall descriptive statistics and correlations for all variables (see S2–S18 Tables in S1 File for descriptive statistics for the individual countries).

https://doi.org/10.1371/journal.pone.0257346.t002

Metric measurement variance, but not scalar measurement invariance could be established for a simple model including the three individual items and no inter-correlations between perceived competence, perceived social relatedness, intrinsic motivation, and procrastination. For these four variables, the metric invariance model had a good absolute fit, whereas the scalar model did not, due to too high RMSEA; moreover, the relative fit was best for the metric model compared to both the configural and scalar model (see S18 Table in S1 File ). Metric, but not scalar invariance could also be established for persistence after modelling residual correlations between items 1 and 2 and items 2 and 3 of the scale. This was necessary due to the similar wording of the items (see “Measures” section for item wordings). Consequently, the same residual correlations were incorporated into all mediation models.

Finally, as the perceived autonomy scale consisted of only two items, it had to be fitted in a model with a correlating factor in order to compute measurement invariance. Both perceived competence and perceived social relatedness were correlated with perceived autonomy ( r = .59** and r = .31**, respectively; see Table 2 ). Therefore, we fit two models combining perceived autonomy with each of these factors; in both cases, metric measurement invariance was established (see S19 Table in S1 File ).

In summary, these results suggest that the meaning of all constructs we aimed to measure was understood similarly by participants across different countries. Consequently, we were able to fit the same mediation model in all countries and compare the resulting path coefficients.

Both gender and age were statistically significantly correlated with perceived competence, perceived social relatedness, intrinsic motivation, procrastination, and persistence (see S20–S22 Tables in S1 File ).

Mediation analyses

Autonomy hypothesis..

We hypothesized that higher perceived autonomy would relate to less procrastination and more persistence, both directly and indirectly (mediated through intrinsic learning motivation). Indeed, perceived autonomy was related negatively to procrastination (H6a) in most countries. Confidence intervals did not include zero in 10 out of 17 countries, all effect estimates were negative and standardized effect estimates ranged from b stand = - .02 to -.46 (see Fig 1 ). Furthermore, perceived autonomy was directly positively related to persistence in most countries. Specifically, for the direct effect of perceived autonomy on persistence (H7a), all but one country (USA, b stand = -.02; p = .621; CI [-.13, .08]) exhibited distinctly positive effect estimates ranging from b stand = .18 to .72 and confidence intervals that did not include zero.

Countries are ordered by sample size from top (highest) to bottom (lowest).

https://doi.org/10.1371/journal.pone.0257346.g001

In terms of indirect effects of perceived autonomy on procrastination mediated by intrinsic motivation (H7a), confidence intervals did not include zero in 8 out of 17 countries and effect estimates were mostly negative, ranging from b stand = -.33 to .03. Indirect effects of perceived autonomy on persistence (mediated by intrinsic motivation; H5a) were distinctly positive and confidence intervals did not include zero in 12 out of 17 countries. The indirect effect estimates and confidence intervals for all remaining countries were consistently positive, with the standardized effect estimates ranging from b stand = .13 to .39, indicating a robust, positive mediated effect of autonomy on persistence. Fig 2 displays the unstandardized path coefficients and their two-sided 5% confidence intervals for the indirect effects of perceived autonomy on procrastination via intrinsic motivation (left) and of perceived autonomy on persistence via intrinsic motivation (right).

https://doi.org/10.1371/journal.pone.0257346.g002

Unstandardized and standardized path coefficients, standard errors, p-values and bias-corrected bootstrapped confidence intervals for the direct and indirect effects of perceived autonomy on procrastination and persistence for each country are provided in S23–S26 Tables in S1 File , respectively.

Competence hypothesis. Secondly, we hypothesized that higher perceived competence would relate to less procrastination and more persistence both directly and indirectly, mediated through intrinsic learning motivation. Direct effects on procrastination (H6b) were negative in most countries and confidence intervals did not include zero in 10 out of 17 countries (see Fig 3 ).

https://doi.org/10.1371/journal.pone.0257346.g003

Standardized effect estimates ranged from b stand = -.02 to -.60, with 10 out of 17 countries exhibiting at least a medium-sized effect. Correspondingly, effect estimates for the direct effects on persistence were positive everywhere except the USA and confidence intervals did not include zero in 14 out of 17 countries (see Fig 3 ). Standardized effect estimates ranged from b stand = -.05 to .64 with 14 out of 17 countries displaying an at least medium-sized positive effect.

The pattern of results for the indirect effects of perceived competence on procrastination mediated by learning motivation (H4b) is illustrated in Fig 4 : Effect estimates were negative with the exception of China and the USA. Confidence intervals did not include zero in 7 out of 17 countries. Standardized effect estimates range between b stand = .06 and -.46. Indirect effects of perceived competence on persistence were positive everywhere except for two countries and confidence intervals did not include zero in 7 out of 17 countries (see Fig 4 ). Standardized effect estimates varied between b stand = -.07 and .46 (see S23–S26 Tables in S1 File for unstandardized and standardized path coefficients).

https://doi.org/10.1371/journal.pone.0257346.g004

Social relatedness hypothesis.

Finally, we hypothesized that stronger perceived social relatedness would be both directly and indirectly (mediated through intrinsic learning motivation) related to less procrastination and more persistence. The pattern of results was more ambiguous here than for perceived autonomy and perceived competence. Direct effect estimates on procrastination (H6c) were negative in 12 countries; however, the confidence intervals included zero in 12 out of 17 countries (see Fig 5 ). Standardized effect estimates ranged from b stand = -.01 to b stand = .33. The direct relation between perceived social relatedness and persistence (H7c) yielded 14 negative and three positive effect estimates. Confidence intervals did not include zero in 7 out of 17 countries (see Fig 5 ), with standardized effect estimates ranging from b stand = -.01 to b stand = .31.

https://doi.org/10.1371/journal.pone.0257346.g005

In terms of indirect effects of perceived social relatedness being related to procrastination mediated by intrinsic motivation (H4c), the pattern of results was consistent: All effect estimates except those for the USA were clearly negative, and confidence intervals did not include zero in 15 out of 17 countries (see Fig 6 ). Standardized effect estimates ranged between b stand = .00 and b stand = -.46. Indirect paths of perceived social relatedness on persistence showed positive effect estimates and standardized effect estimates ranging from b stand = .00 to .44 and confidence intervals not including zero in 16 out of 17 countries (see Fig 6 ; see S23–S26 Tables in S1 File for unstandardized and standardized path coefficients).

https://doi.org/10.1371/journal.pone.0257346.g006

Meta-analytic approach

Due to the overall similarity of the results across many countries, we decided to compute, in an additional, exploratory step, the same models with path estimates fixed across countries. This resulted in three models with average path estimates across the entire sample. Standardized path coefficients for the direct and indirect effects of the basic psychological needs on procrastination and persistence are presented in S27 and S28 Tables in S1 File , respectively. We compared the model fits of these three average models to those of the multigroup mediation models: If the fit of the average model is better than that of the multigroup model, it indicates that the individual countries are similar enough to be combined into one model. The amount of explained variance per model, outcome variable and country are provided in S29 Table in S1 File for procrastination and S30 Table in S1 File for persistence.

Perceived autonomy.

Relative model fit was better for the perceived autonomy model with fixed paths (BIC = 432,707.89) compared to the multigroup model (BIC = 432,799.01). Absolute model fit was equally good in the multigroup model (RMSEA = 0.05, CFI = 0.98, TLI = 0.97) and in the fixed path model (RMSEA = 0.05, CFI = 0.97, TLI = 0.97). Consequently, the general model in Fig 7 describes the data from all 17 countries equally well. The average amount of explained variance, however, is slightly higher in the multigroup model, with 19.9% of the variance in procrastination and 33.7% of the variance in persistence explained, as compared to 18.3% and 27.6% in the fixed path model. The amount of variance explained increased substantially in some countries when fixing the paths: in the multigroup model, explained variance ranges from 2.2% to 44.4% for procrastination and from 0.9% to 69.9% for persistence, compared to 13.0% - 27.7% and 18.2% to 63.2% in the fixed path model. Notably, the amount of variance explained did not change much in the three countries with the largest samples, Austria, Sweden, and Finland; countries with much smaller samples and larger confidence intervals were more affected.

*** p = < .001.

https://doi.org/10.1371/journal.pone.0257346.g007

Overall, perceived autonomy had significant direct and indirect effects on both procrastination and persistence; higher perceived autonomy was related to less procrastination directly ( b unstand = -.27, SE = .02, p = < .001) and mediated by learning motivation ( b unstand = -.20, SE = .01, p = < .001) and to more persistence directly ( b unstand = .24, SE = .01, p = < .001) and mediated by learning motivation ( b unstand = .12, SE = .01, p = < .001). Direct effects for the autonomy model are shown in Fig 7 ; for the indirect effects see Table 3 .

https://doi.org/10.1371/journal.pone.0257346.t003

Effects of age and gender varied across countries (see S20 Table in S1 File ).

Perceived competence.

For the perceived competence model, relative fit decreased when fixing the path coefficient estimates (BIC = 465,830.44 to BIC = 466,020.70). The absolute fit indices were also better for the multigroup model (RMSEA = 0.05, CFI = 0.97, TLI = 0.96) than for the fixed path model (RMSEA = 0.06, CFI = 0.96, TLI = 0.96). Hence, multigroup modelling describes the data across all countries somewhat better than a fixed path model as depicted in Fig 8 . Correspondingly, the fixed path model explained less variance on average than did the multigroup model, with 23.2% instead of 24.3% of the variance in procrastination and 32.9% instead of 37.3% of the variance in persistence explained. Explained variance ranged from 1.0% to 51.9% for procrastination in the multigroup model, as compared to 13.9% - 34.4% in the fixed path model. The amount of variance in persistence explained ranged from 1.0% to 58.1% in the multigroup model and from 23.5% to 55.9% in the fixed path model (see S29 and S30 Tables in S1 File ).

https://doi.org/10.1371/journal.pone.0257346.g008

Overall, higher perceived competence was related to less procrastination ( b unstand = -.44, SE = .02, p = < .001) and to higher persistence ( b unstand = .32, SE = .01, p = < .001). These effects were partly mediated by intrinsic learning motivation ( b unstand = -.11, SE = .01, p = < .001, and b unstand = .07, SE = .01, p = < .001, respectively; see Table 3 ). Effects of gender and age varied between countries, see S21 Table in S1 File .

Perceived social relatedness.

Finally, the perceived social relatedness model with fixed paths had a relatively better model fit (BIC = 479,428.46) than the multigroup model (BIC = 479,604.61). Likewise, the absolute model fit was similar in the model with path coefficients fixed across countries (RMSEA = 0.05, CFI = 0.97, TLI = 0.96) and the multigroup model (RMSEA = 0.05, CFI = 0.97, TLI = 0.97). The multigroup model explained 17.6% of the variance in procrastination and 26.3% of the variance in persistence, as compared to 15.2% and 21.6%, respectively in the fixed path model. Explained variance for procrastination ranged between 0.5% and 48.1% in the multigroup model, and from 9.0% to 23.0% in the fixed path model. Similarly, the multigroup model explained between 1.0% and 56.5% of the variance in persistence across countries, while the fixed path model explained between 15.6% and 48.3% (see S29 and S30 Tables in S1 File ).

Hence, the fixed path model depicted in Fig 9 is well-suited for describing data across all 17 countries. Higher perceived social relatedness is related to less procrastination both directly ( b unstand = -.06, SE = .01, p = < .001) and indirectly through learning motivation ( b unstand = -.12, SE = .01, p = < .001). Likewise, it is related to higher persistence both directly ( b unstand = .07, SE = .01, p = < .001) and indirectly through learning motivation ( b unstand = .08, SE = .00, p = < .001; see Table 3 ). Effects of gender and age are shown in S22 Table in S1 File .

https://doi.org/10.1371/journal.pone.0257346.g009

The aim of this study was to extend current research on the association between the basic psychological needs for autonomy, competence, and social relatedness with intrinsic motivation and two important aspects of learning behavior—procrastination and persistence—in the new and unique situation of pandemic-induced distance learning. We also investigated SDT’s [ 7 ] postulate that the relation between basic psychological need satisfaction and active (persistence) as well as passive (procrastination) learning behavior is mediated by intrinsic motivation. To test the theory’s underlying claim of universality, we collected data from N = 15,462 students across 17 countries in Europe, Asia, and North America.

Confirming our hypothesis, we found that the three basic psychological needs were consistently and positively related to intrinsic motivation in all countries except for the USA (H1a - c). This consistent result is in line with self-determination theory [ 7 ] and other previous studies (e.g., 9), which have found that satisfaction of the three basic needs for autonomy, competence and social relatedness is related to higher intrinsic motivation. Notably, the association with intrinsic motivation was stronger for perceived autonomy and perceived competence than for perceived social relatedness. This also has been found in previous studies [ 4 , 9 , 28 ]. Pandemic-induced distance learning, where physical and subsequential social contact in all areas of life was severely constricted, might further exacerbate this discrepancy, as instructors may have not been able to establish adequate communication structures due to the rapid switch to distance learning [ 36 , 53 ]. As hypothesized, intrinsic motivation was in general negatively related to procrastination (H2a - c) and positively related to persistence (H3a - c), indicating that students who are intrinsically motivated are less prone to procrastination and more persistent when studying. This again underlines the importance of intrinsic motivation for adaptive learning behavior, even and particularly in a distance learning setting, where students are more prone to disengage from classes [ 34 ].

The mediating effect of intrinsic motivation on procrastination and persistence

Direct effects of the basic needs on the outcomes were consistently more ambiguous (with smaller effect estimates and larger confidence intervals, including zero in more countries) than indirect effects mediated by intrinsic motivation. This difference was particularly pronounced for perceived social relatedness, where a clear negative direct effect on procrastination (H6c) could be observed only in the three countries with the largest sample size (Austria, Sweden, Finland) and Romania, whereas the confidence interval in most countries included zero. Moreover, in Estonia there was even a clear positive effect. The unexpected effect in the Estonian sample may be attributed to the fact that this country collected data only from international exchange students. Since the lockdown in Estonia was declared only a few weeks after the start of the semester, international exchange students had only a very short period of time to establish contacts with fellow students on site. Accordingly, there was probably little integration into university structures and social contacts were maintained more on a personal level with contacts from the home country. Thus, such students’ fulfillment of this basic need might have required more time and effort, leading to higher procrastination and less persistence in learning.

A diametrically opposite pattern was observed for persistence (H7c), where some direct effects of social relatedness were unexpectedly negative or close to zero. We therefore conclude that evidence for a direct negative relationship between social relatedness and procrastination and a direct positive relationship between social relatedness and persistence is lacking. This could be due to the specificity of the COVID-19 situation and resulting lockdowns, in which maintaining social contact took students’ focus off learning. In line with SDT, however, indirect effects of perceived social relatedness on procrastination (H4c) and persistence (H5c) mediated via intrinsic motivation were much more visible and in the expected directions. We conclude that, while the direct relation between perceived social relatedness and procrastination is ambiguous, there is strong evidence that the relationship between social relatedness and the measured learning behaviors is mediated by intrinsic motivation. Our results strongly underscore SDT’s assumption that close social relations promote intrinsic motivation, which in turn has a positive effect on learning behavior (e.g., [ 6 , 14 ]). The effects for perceived competence exhibited a somewhat clearer and hypothesis-conforming pattern. All direct effects of perceived competence on procrastination (H6b) were in the expected negative direction, albeit with confidence intervals spanning zero in 7 out of 17 countries. Direct effects of perceived competence on persistence (H7b) were consistently positive with the exception of the USA, where we observed a very small and non-significant negative effect. Indirect effects of perceived competence on procrastination (H4b) and persistence (H5b) as mediated by intrinsic motivation were mostly consistent with our expectations as well. Considering this overall pattern of results, we conclude that there is strong evidence that perceived competence is negatively associated with procrastination and positively associated with persistence. Furthermore, our results also support SDT’s postulate that the relationship between perceived competence and the measured learning behaviors is mediated by intrinsic motivation.

It is notable that the estimated direct effects of perceived competence on procrastination and persistence were higher than the indirect effects in most countries we investigated. Although SDT proposes that perceived competence leads to higher intrinsic motivation, Deci and Ryan [ 8 ] also argue that it affects all types of motivation and regulation, including less autonomous forms such as introjected and identified motivation, indicating that if the need for competence is not satisfied, all types of motivation are negatively affected. This may result in a general amotivation and lack of action. In our study, we only investigated intrinsic motivation as a mediator. For future research, it might be advantageous to further differentiate between different types of externally and internally controlled behavior. Furthermore, perceived competence increases when tasks are experienced as optimally challenging [ 7 , 54 ]. However, in order for instructors to provide the optimal level of difficulty and support needed, frequent communication with students is essential. Considering that data collection for the present study took place at a time of great uncertainty, when many countries had only transitioned to distance learning a few weeks prior, it is reasonable to assume that both structural support as well as communication and feedback mechanisms had not yet matured to a degree that would favor individualized and competency-based work.

However, our findings corroborate those from earlier studies insofar as they underline the associations between perceived competence and positive learning behavior (e.g., [ 19 ]), that is, lower procrastination [ 18 ] and higher persistence (e.g., [ 21 ]), even in an exceptional situation like pandemic-induced distance learning.

Turning to perceived autonomy, although the confidence intervals for the direct effects of perceived autonomy on procrastination (H6a) did span zero in most countries with smaller sample sizes, all effect estimates indicated a negative relation with procrastination. We expected these relationships from previous studies [ 18 , 23 ]; however, the effect might have been even more pronounced in the relatively autonomous learning situation of distance learning, where students usually have increased autonomy in deciding when, where, and how to learn. While this bears the risk of procrastination, it also comes with the opportunity to consciously delay less pressing tasks in favor of other, more important or urgent tasks (also called strategic delay ) [ 5 ], resulting in lower procrastination. In future studies, it might be beneficial to differentiate between passive forms of procrastination and active strategic delay in order to obtain more detailed information on the mechanisms behind this relationship. Direct effects of autonomy on persistence (H7a) were consistently positive. Students who are free to choose their preferred time and place to study may engage more with their studies and therefore be more persistent.

Indirect effects of perceived autonomy on procrastination mediated by intrinsic motivation (H4a) were negative in all but two countries (China and the USA), which is generally consistent with our hypothesis and in line with previous research (e.g., [ 23 ]). Additionally, we found a positive indirect effect of autonomy on persistence (H5a), indicating that autonomy and intrinsic motivation play a crucial role in students’ persistence in a distance learning setting. Based on our results, we conclude that perceived autonomy is negatively related to procrastination and positively related to persistence, and that this relationship is mediated by intrinsic motivation. It is worth noting that, unlike with perceived competence, the direct and indirect effects of perceived autonomy on the outcomes procrastination and persistence were similarly strong, suggesting that perceived autonomy is important not only as a driver of intrinsic motivation but also at a more direct level. It is important to make the best possible use of the opportunity for greater autonomy that distance learning offers. However, autonomy is not to be equated with a lack of structure; instead, learners should be given the opportunity to make their own decisions within certain framework conditions.

The applicability of self-determination theory across countries

Overall, the results of our mediation analysis for the separate countries support the claim posited by SDT that basic need satisfaction is essential for intrinsic motivation and learning across different countries and settings. In an exploratory analysis, we tested a fixed path model including all countries at once, in order to test whether a simplified general model would yield a similar amount of explained variance. For perceived autonomy and social relatedness, the model fit increased, whereas for perceived competence it decreased slightly compared to the multigroup model. However, all fixed path models exhibited adequate model fit. Considering that the circumstances in which distance learning took place in different countries varied to some degree (see also Limitations), these findings are a strong indicator for the universality of SDT.

Study strengths and limitations

Although the current study has several strengths, including a large sample size and data from multiple countries, three limitations must be considered. First, it must be noted that sample sizes varied widely across the 17 countries in our study, with one country above 6,000 (Austria), two above 1,000 (Finland and Sweden) and the rest ranging between 104 and 905. Random sampling effects are more problematic in smaller samples; hence, this large variation weakens our ability to conduct cross-country comparisons. At the same time, small sample sizes weaken the interpretability of results within each country; thus, our results for Austria, Finland and Sweden are considerably more robust than for the remaining fourteen countries. Additionally, two participating countries collected specific subsamples: In China, participants were only recruited from one university, a nursing school. In Estonia, only international exchange students were invited to participate. Nevertheless, with the exception of the unexpected positive direct relationship between social relatedness and procrastination, all observed divergent effects were non-significant. Indeed, this adds to the support for SDT’s claims to universality regarding the relationship between perceived autonomy, competence, and social relatedness with intrinsic motivation: Results in the included countries were, despite their differing subsamples, in line with the overall trend of results, supporting the idea that SDT applies equally to different groups of learners.

Second, due to the large number of countries in our sample and the overall volatility of the situation, learning circumstances were not identical for all participants. Due to factors such as COVID-19 case counts and national governments’ political priorities, lockdown measures varied in their strictness across settings. Some universities were fully closed, some allowed on-site teaching for particular groups (e.g., students in the middle of a laboratory internship), and some switched to distance learning but held exams on site (see S1 Table in S1 File for further information). Therefore, learning conditions were not as comparable as in a strict experimental setting. On the other hand, this strengthens the ecological validity of our study. The fact that the pattern of results was similar across contexts with certain variation in learning conditions further supports the universal applicability of SDT.

Finally, due to the novelty of the COVID-19 situation, some of the measures were newly developed for this study. Due to the need to react swiftly and collect data on the constantly evolving situation, it was not possible to conduct a comprehensive validation study of the instruments. Nevertheless, we were able to confirm the validity of our instruments in several ways, including cognitive interview testing, CFAs, CR, and measurement invariance testing.

Conclusion and future directions

In general, our results further support previous research on the relation between basic psychological need fulfilment and intrinsic motivation, as proposed in self-determination theory. It also extends past findings by applying this well-established theory to the new and unique situation of pandemic-induced distance learning across 17 different countries. Moreover, it underlines the importance of perceived autonomy and competence for procrastination and persistence in this setting. However, various other directions for further research remain to be pursued. While our findings point to the relevance of social relatedness for intrinsic motivation in addition to perceived competence and autonomy, further research should explore the specific mechanisms necessary to promote social connectedness in distance learning. Furthermore, in our study, we investigated intrinsic motivation, as the most autonomous form of motivation. Future research might address different types of externally and internally regulated motivation in order to further differentiate our results regarding the relations between basic need satisfaction and motivation. Finally, a longitudinal study design could provide deeper insights into the trajectory of need satisfaction, intrinsic motivation and learning behavior during extended periods of social distancing and could provide insights into potential forms of support implemented by teachers and coping mechanisms developed by students.

Supporting information

https://doi.org/10.1371/journal.pone.0257346.s001

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

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

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Farah lalani.

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Stay up to date:.

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

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

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

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

How is the education sector responding to COVID-19?

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

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

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

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

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

What does this mean for the future of learning?

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

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

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

These 3 charts show the global growth in online learning

The challenges of online learning.

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

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

Is learning online as effective?

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

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

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

A changing education imperative

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

The importance of disseminating knowledge is highlighted through COVID-19

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

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A new report found that the heavy dependence on technology during the pandemic caused “staggering” education inequality. What was your experience?

By Natalie Proulx

During the coronavirus pandemic, many schools moved classes online. Was your school one of them? If so, what was it like to attend school online? Did you enjoy it? Did it work for you?

In “ Dependence on Tech Caused ‘Staggering’ Education Inequality, U.N. Agency Says ,” Natasha Singer writes:

In early 2020, as the coronavirus spread, schools around the world abruptly halted in-person education. To many governments and parents, moving classes online seemed the obvious stopgap solution. In the United States, school districts scrambled to secure digital devices for students. Almost overnight, videoconferencing software like Zoom became the main platform teachers used to deliver real-time instruction to students at home. Now a report from UNESCO , the United Nations’ educational and cultural organization, says that overreliance on remote learning technology during the pandemic led to “staggering” education inequality around the world. It was, according to a 655-page report that UNESCO released on Wednesday, a worldwide “ed-tech tragedy.” The report, from UNESCO’s Future of Education division, is likely to add fuel to the debate over how governments and local school districts handled pandemic restrictions, and whether it would have been better for some countries to reopen schools for in-person instruction sooner. The UNESCO researchers argued in the report that “unprecedented” dependence on technology — intended to ensure that children could continue their schooling — worsened disparities and learning loss for hundreds of millions of students around the world, including in Kenya, Brazil, Britain and the United States. The promotion of remote online learning as the primary solution for pandemic schooling also hindered public discussion of more equitable, lower-tech alternatives, such as regularly providing schoolwork packets for every student, delivering school lessons by radio or television — and reopening schools sooner for in-person classes, the researchers said. “Available evidence strongly indicates that the bright spots of the ed-tech experiences during the pandemic, while important and deserving of attention, were vastly eclipsed by failure,” the UNESCO report said. The UNESCO researchers recommended that education officials prioritize in-person instruction with teachers, not online platforms, as the primary driver of student learning. And they encouraged schools to ensure that emerging technologies like A.I. chatbots concretely benefited students before introducing them for educational use. Education and industry experts welcomed the report, saying more research on the effects of pandemic learning was needed. “The report’s conclusion — that societies must be vigilant about the ways digital tools are reshaping education — is incredibly important,” said Paul Lekas, the head of global public policy for the Software & Information Industry Association, a group whose members include Amazon, Apple and Google. “There are lots of lessons that can be learned from how digital education occurred during the pandemic and ways in which to lessen the digital divide. ” Jean-Claude Brizard, the chief executive of Digital Promise, a nonprofit education group that has received funding from Google, HP and Verizon, acknowledged that “technology is not a cure-all.” But he also said that while school systems were largely unprepared for the pandemic, online education tools helped foster “more individualized, enhanced learning experiences as schools shifted to virtual classrooms.” ​Education International, an umbrella organization for about 380 teachers’ unions and 32 million teachers worldwide, said the UNESCO report underlined the importance of in-person, face-to-face teaching. “The report tells us definitively what we already know to be true, a place called school matters,” said Haldis Holst, the group’s deputy general secretary. “Education is not transactional nor is it simply content delivery. It is relational. It is social. It is human at its core.”

Students, read the entire article and then tell us:

What findings from the report, if any, surprised you? If you participated in online learning during the pandemic, what in the report reflected your experience? If the researchers had asked you about what remote learning was like for you, what would you have told them?

At this point, most schools have returned to in-person teaching, but many still use technology in the classroom. How much tech is involved in your day-to-day education? Does this method of learning work well for you? If you had a say, would you want to spend more or less time online while in school?

What are some of the biggest benefits you have seen from technology when it comes to your education? What are some of the biggest drawbacks?

Haldis Holst, UNESCO’s deputy general secretary, said: “The report tells us definitively what we already know to be true, a place called school matters. Education is not transactional nor is it simply content delivery. It is relational. It is social. It is human at its core.” What is your reaction to that statement? Do you agree? Why or why not?

As a student, what advice would you give to schools that are already using or are considering using educational technology?

Students 13 and older in the United States and Britain, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public and may appear in print.

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Natalie Proulx joined The Learning Network as a staff editor in 2017 after working as an English language arts teacher and curriculum writer. More about Natalie Proulx

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Original research article, impact of the covid-19 pandemic on online learning in higher education: a bibliometric analysis.

• 1 Faculty of Public Administration, University of Ljubljana, Ljubljana, Slovenia
• 2 Department of Primary Level Education, University of the Aegean, Rhodes, Greece

The outbreak of the COVID-19 pandemic significantly disrupted higher education by forcing the transition to online learning, which became a mandatory teaching process during the lockdowns. Although the epidemiological situation has gradually improved since then, online learning is becoming ever more popular as it provides new learning opportunities. Therefore, the paper aims to present recent research trends concerning online learning in higher education during the COVID-19 pandemic by using selected bibliometric approaches. The bibliometric analysis is based on 8,303 documents from the Scopus database published between January 2020 and March 2022, when repeated lockdowns meant most countries were experiencing constant disruptions to the educational process. The results show that the COVID-19 pandemic increased interest in online learning research, notably in English-speaking and Asian countries, with most research being published in open-access scientific journals. Moreover, the topics most frequently discussed in the online learning research during the COVID-19 pandemic were ICT and pedagogy, technology-enhanced education, mental health and well-being, student experience and curriculum and professional development. Finally, the COVID-19 pandemic encouraged explorations of emergency remote learning approaches like e-learning, distance learning and virtual learning, which are intended to limit physical contact between teachers and students, where the specific requirements of a given field of study often guide which online learning approach is the most suitable. The findings add to the existing body of scientific knowledge and support the evidence-based policymaking needed to ensure sustainable higher education in the future.

1. Introduction

The outbreak of the COVID-19 pandemic significantly disrupted higher education by forcing the transition to online learning, which became a mandatory teaching process during the lockdowns ( Aristovnik et al., 2020a ). Despite the educational process saw disruptions on all levels of education, i.e., primary, secondary and tertiary ( Tang, 2023 ), as well as in adult education ( James and Thériault, 2020 ), worker education ( Dedeilia et al., 2023 ) and lifelong education ( Waller et al., 2020 ), higher education students proved to be one of the worst affected groups because the social distancing measures, on top of their education, challenged their financial and housing situation ( Aristovnik et al., 2020a ). Challenges arising from the density of students in educational facilities (e.g., campuses, faculties, dormitories etc.) meant higher education institutions were forced to offer education relying on various information and communication technologies (ICTs) and tried to ensure education comparable in quality to traditional learning, noting that the quality of online learning delivery holds important implications for student satisfaction and student performance ( Keržič et al., 2021 ). Nevertheless, the lockdown periods were devastating for many students also in terms of their emotional functioning ( Raccanello et al., 2022 ). The COVID-19 pandemic eventually grew more predictable and manageable, allowing higher education institutions to gradually shift back to traditional learning approaches. Although the epidemiological situation has improved over time, online learning is becoming increasingly popular as it provides new learning opportunities, especially when combined with traditional learning.

The rapid, yet from the health protection point of view necessary ( Aristovnik et al., 2020b ), shift from traditional learning to online learning considerably affected teaching and learning. The transition to online learning was made without adequate consideration of whether the study materials and teaching methods were suitable for this mode of higher education delivery. This was an ad hoc shift in a situation of great uncertainty for both teachers and students. The transition to online learning has also brought to the surface gaps in higher education providers’ preparedness and their lack of ICT infrastructure, resulting in unequal access to quality education for all, particularly students from rural areas and regions with lower socio-economic development. It is important to note here that the rapid shift to an online learning environment in emergency circumstances should not be confused with properly planned online education equipped with appropriate infrastructure that enables and supports pedagogical work and study in an online environment ( Hodges et al., 2020 ; Fuchs, 2022 ; Misiejuk et al., 2023 ). Apart from the changes in teaching and learning, the social aspect of students’ lives has been affected as well. The most worrying consequence has been social isolation leading to a lack of crucial social interaction for students ( Elmer et al., 2020 ; Bonsaksen et al., 2021 ; Fried et al., 2021 ; Van der Graaf et al., 2021 ) and in some cases also in coronavirus-related post-traumatic stress syndrome (PTSD) ( Ochnik et al., 2021 ). According to Gavriluţă et al. (2022) , three dimensions affected students during the COVID-19 pandemic: educational, social, and emotional. The transition from traditional to online learning entailed a significant transformation in education, requiring changes in teaching practices and new learning approaches. Further, the social aspect of the COVID-19 pandemic and associated lockdowns is evident in the absence of relational, economic and professional problems (in)directly affecting the transition to adulthood. The new reality changed attitudes to various aspects of life and, in turn, also affected emotional responsiveness. Briefly, substantial changes to everyday student lives were made during the COVID-19 pandemic that may hold far-reaching effects of currently unknown scope in the near and distant future ( Campos et al., 2022 ; Gao et al., 2022 ; Keržič et al., 2022 ; Rasli et al., 2022 ).

Therefore, the educational community requires greater insights into different aspects of the COVID-19 pandemic’s impact on online learning, e.g., students, teachers, pedagogy, ICT technology, online learning approaches and implications for various fields of study. In the context of higher education, some bibliometric studies (e.g., Gurcan et al., 2022 ; Saqr et al., 2023 ) have already sought to address issues involving online learning during the pandemic. Yet, they relied on a limited and narrow bibliographic dataset of peer-reviewed literature or lacked a qualitative synthesis of the results beyond the metrics, thereby neglecting some general comprehensive outlines of the global research into the topic ( Saqr et al., 2023 ). Moreover, despite some bibliometric studies focusing on technical aspects (e.g., Navarro-Espinosa et al., 2021 ; Bozkurt, 2022 ; Tlili et al., 2022 ), the identification of the most effective ICT tools for specific online learning approaches remains unclear. Finally, there are also some bibliometric studies that attempt to determine the effectiveness of online learning in providing higher education ( Brika et al., 2021 ; Baber et al., 2022 ; Bilal et al., 2022 ; Bozkurt, 2022 ; Fauzi, 2022 ; Küçük-Avci et al., 2022 ; Yan et al., 2022 ), however, they often overlook the specific requirements of individual fields of study, thereby neglecting the crucial aspect of tailoring online learning provision to different disciplines.

The bibliometric study presented in the paper accordingly aims to fill the presented gaps in the literature. Specifically, it aims to present a global overview of the recent research trends in online learning in higher education using a comprehensive dataset of literature encompassing different varieties of online learning approaches that can facilitate online learning during the COVID-19 pandemic, provide some relevant qualitative synthesis of the results beyond the metrics and examine the relationships between ICT tools, online learning approaches and fields of study. Thus, the present bibliometric study, focusing on higher education, tries to answer the following three research questions:

• RQ1: What is the current state of the online learning research by conducting a descriptive overview and identifying top-cited documents?

• RQ2: What is the scientific production of online learning research across countries and sources?

• RQ3: Which are the main research hotspots and concepts in online learning research?

The remainder of the paper is structured as follows. The next section provides a literature review of recent bibliometric studies. The following section outlines the materials and methods applied in the study before the results of the present bibliometric analysis are described in the next section. At the end, the final section provides a discussion and conclusion while summarizing the main findings and implications.

2. Literature review

The outbreak of the COVID-19 pandemic led many governments to expand the use of online learning approaches as a solution to the global health challenge. Researchers thus showed rising interest in investigating the field of online learning, its dimensions, and its trends on all levels of education, particularly higher education. Such research relied heavily on bibliometric approaches to analyzing scientific research in the higher education context. Pham et al. (2022) concluded based on the 414 articles that although in the decades prior, there was an increase in the number of articles touching on the components of e-learning, such as the learning management system, this rise was accelerated during the pandemic in both developed and developing countries. This may be attributed to the attention of governmental policies that considered the topic of e-learning to be critical and worthy of priority. Similarly, Fauzi (2022) investigated 1,496 articles and concluded that the research focused on a few specific topics. The first is the delivery factor, which refers to selecting the appropriate learning practices. The second is the health and safety factor that relates to minimizing any risk that e-learning could bring to the mental and physical health of learners or teachers, such as stress, anxiety or even depression. The third topic refers to the field of study and the impact of e-learning. In areas like medical education, where clinical activities and labs have to be attended in person, some online learning approaches might be less appropriate than when used in other areas, such as social studies, where the requirements are less complex or different. Zhang et al. (2022) confirmed this finding after performing bibliometric research on 1,061 articles published between January 2020 and August 2021. They explained that theorists and researchers showed a growing interest in ways to respond to crises, such as the pandemic, and how to develop the best practices to ensure the quality and efficiency of e-learning. Examples of such practices might be inquiry-oriented learning and hands-on activities. This could derive from the already existing tendency of education researchers to respond to unprecedented global challenges or changes. The authors explain that this conclusion addresses interest in e-learning practices holistically.

In the same context, Yan et al. (2022) employed a bibliometric approach and identified that various digital tools are used in e-learning in the field of health studies. After investigating 132 studies, they concluded that selecting appropriate tools depends on many factors, including the field of a given course, the aims, and their effectiveness. They add that these findings can be significant for groups of people such as experts or trainee teachers. Okoro et al. (2022) researched 1,722 articles published between 2012 and 2021 and detected a surge in interest in the mental health of postgraduate students, as revealed by the research trends discussed in these articles. Still, they describe this surge as having been greater between 2020 and 2021, which may be attributed to the COVID-19 restrictions and their implications. Moreover, they believe that this research focus will likely continue soon.

After looking at 2,307 articles published between 2017 and 2021, Baber et al. (2022) detected an increasing trend in researching digital literacy. While this was underway before the pandemic, the latter caused a statistically significant further surge. Digital literacy is approached in the studied articles through parameters like instruction, teachers, learners, ICT and its applications, content knowledge, competencies, skills, perceptions, and higher education. It is also associated with acquiring the qualities required to deal with topics such as misinformation, fake news, technological content knowledge, health literacy, COVID-19, and distance education. The authors state that their study identified dynamics hidden in these research trends, which will likely continue in the next few years.

In higher education specifically, based on 602 articles, Brika et al. (2021) corroborated the growing trend of publishing articles on e-learning during the pandemic and outlined certain sub-topics of it, namely: motivation and students’ attitudes; blended and virtual learning comparison; types of online assessment; stress, anxiety and mental health; strategies to improve learners’ skills; quality; performance of the education delivered; challenges; and the potential of technology to lead to change and reform of higher education syllabi or curricula. The scope of those articles was to paint a bigger picture of how higher education communities and institutions use and treat online learning. This is expected to help with efficient decision-making in the future in order to have better results and functions in higher education and appropriate response to crises.

The bibliometric studies carried out during the pandemic identified a trend among researchers in higher education institutions to investigate more the technology factor and how the progress of the Internet, along with information and communication technologies generally, can further assist new modes of learning, such as online learning and distance learning. This might be attributed to a vision for a better means for new types of learning, as Küçük-Avci et al. (2022) claimed after carrying out a bibliometric analysis of 1,547 articles published between 2020 and 2021. The authors detected certain trends regarding distance learning in higher education. A main finding of their study, along with the increase in studies on distance education and e-learning in higher education, is that before the pandemic, the fact that these approaches were not so mandatory meant there was greater efficiency, probably due to the learners’ motivation. The authors further claim that researchers show a stronger interest in the technological means that can assist these types of learning. In addition, while researching 1,986 articles, Bozkurt (2022) established an increase in the implementation of blended learning by researchers who also aim to investigate the relationship between technological applications and learning institutions. Within these tendencies, researchers consider four thematic fields: a comparison of online and onsite learning with regard to effectiveness and efficiency; the experience, impressions and attitudes of stakeholders and learning community members with respect to blended learning; teacher training and curriculum development that will assure the appropriate and challenge-free implementation of blended learning; and the use of mostly a quantitative approach to research of blended learning.

Bilal et al. (2022) also examined research trends concerned with e-learning in higher education during the COVID-19 period by researching 1,595 studies published between 2020 and 2021. The four main trends they identified were supplementary to those mentioned by other authors: the first is about the challenges regarding online learning or blended learning along with the appropriate strategies in response; the second is student-centered collaborative learning and appropriate curriculum design; the third concerns home-based learning through a type of laboratory and the general conditions surrounding it; and the fourth addresses teachers’ background, training, professional competencies and interdisciplinary learning.

Tlili et al. (2022) focused on mapping COVID-19’s impact on Massive Open Online Courses (MOOCs). The overall finding from the 108 articles they considered is that there has been growing interest in these courses generally, and more specifically in research around their function and quality. This interest encompasses the main features of such courses, which provide easy accessibility and flexibility. However, they noted that this interest followed another trend among researchers in the context. In other words, the countries that published on MOOCs before the pandemic are the same countries that published during the period under study. Moreover, they stated that there is interest in the technical characteristics and requirements of such courses. Finally, the authors concluded that although most MOOCs were ICT courses, research has escalated into courses that refer to business, personal development or the humanities.

Several conclusions can be drawn from the above bibliometric studies. First, the series of bibliometric studies conducted during the pandemic demonstrates the rise of interest in online learning in higher education during the pandemic. Of course, there was a tendency toward e-learning before the pandemic, but between 2020 and 2022, this seems to have accelerated. The phenomenon is more intense in countries such as the USA, Canada, Australia, the UK, India and China. Concerning the area of study, the focus of researchers appears to be greater in fields such as Engineering, Sciences, and Health Sciences, albeit all fields seem to be investigated ( Djeki et al., 2022 ; Pham et al., 2022 ; Vaicondam et al., 2022 ; Zhang et al., 2022 ). Various studies have focused on determining the effectiveness of e-learning classes and courses or pointing out parameters that influence their effectiveness. These could be the appropriate conditions or subtopics like motivation, blended learning, learning tools, teacher training, cooperation between different institutions or efficient practices ( Brika et al., 2021 ; Baber et al., 2022 ; Bilal et al., 2022 ; Bozkurt, 2022 ; Fauzi, 2022 ; Küçük-Avci et al., 2022 ; Yan et al., 2022 ). A specific trend of authors is to examine virtual classes and laboratories ( Kartimi et al., 2022 ; Rojas-Sánchez et al., 2022 ; Zhang et al., 2022 ). Finally, there is a focus on the technology factor. Namely, researchers have concentrated on technical issues and conditions related to e-learning courses and their proper functioning ( Navarro-Espinosa et al., 2021 ; Bozkurt, 2022 ; Tlili et al., 2022 ).

3. Materials and methods

Comprehensive bibliometric data on online learning research during the COVID-19 pandemic were retrieved on 1 March 2022 from Scopus, a world-leading bibliographic database of peer-reviewed literature. The Scopus database was preferred because it has a broader coverage of scientific research than other databases such as Web of Science ( Falagas et al., 2008 ). This was confirmed by an initial search using the same search query in each database, revealing that Scopus provided more relevant documents than Web of Science. Moreover, compared to the Scopus database, the Web of Science has been found to be a database that significantly underrepresents the scientific disciplines of the Social Sciences and the Arts and Humanities ( Mongeon and Paul-Hus, 2016 ). Although English dominates in both Scopus and Web of Science, Scopus generally offers wider coverage of non-English documents, given that the titles, abstracts, and keywords are in English ( Vera-Baceta et al., 2019 ). According to the basic statistical theory, which can also be applied in the context of bibliometric analysis, larger samples lead to analytical outcomes that are likely to be more accurate ( Rogers et al., 2020 ). Therefore, Scopus appears to be a more relevant bibliographic database meeting the specifics of online learning research during the COVID-19 pandemic.

The search strategy was based on title, abstract, and keywords search using the advanced search engine and the search query covered keywords related to different online learning types (using the Boolean operator ‘OR’) and the COVID-19 pandemic (using the Boolean operator ‘AND’). The search was further limited to the period 2020–2022 (using the Boolean operator ‘AND’) to capture documents published between January 2020 and March 2022, when most countries were experiencing constant disruptions in the educational process imposed by repeated lockdowns. As the search query had no language restrictions, the full text of the obtained documents can be in any language, provided that the titles, abstracts, and keywords are in English. Therefore, the language has no impact on the results, as the bibliometric analysis is conducted solely based on the titles, abstracts, and keywords of the documents. According to the presented search query, 9,921 documents were obtained. After further revising the obtained documents, it was identified that some of them are not explicitly related to the context of higher education. By machine screening of documents by title, abstract, and keywords, those related to lower levels of education (i.e., primary and secondary education), as well as adult and worker education (i.e., lifelong education), were excluded from the database. There were 1,618 or 16% of such documents. The remaining 8,303 documents were identified as eligible for further bibliometric examination of online learning research during the COVID-19 pandemic. The bibliometric analysis utilized several bibliometric approaches ( Figure 1 ).

Figure 1 . Bibliometric approaches used in the bibliometric analysis. Own elaboration.

First, a descriptive overview was conducted to examine particular general bibliometric items, including timespan, number of (all, cited, single-authored) documents, authors, sources and author keywords and authors, references, and citations per document as well as to identify the most relevant documents. Scientific production was also examined to determine the most relevant countries and sources. Finally, network analysis was performed to identify the research hotspots according to the keyword co-occurrence network and examine the relationship between the main concepts based on a three-field plot analysis. The presented bibliometric approaches required the use of several different software tools. The descriptive overview was conducted using the Python Data Analysis Library Pandas ( McKinney, 2012 ), scientific production was visualized by the Python Visualization Library Matplotlib ( Hunter, 2007 ), while network analysis was performed using VOSviewer (keyword co-occurrence) ( Van Eck and Waltman, 2010 ) and the Python Visualization Library Plotly (a three-field plot) ( Pandey and Panchal, 2020 ). Specifically, the calculation for the three-field plot analysis included the following steps. Suppose that C 1 , C 2 , … , C m are analysed concepts where each concept C i is defined by a set of keywords and represented by binary indicators W i 1 , W i 2 , … , W i k i , expressed as C i = max j = 1 , … , k i W i j for i = 1 , … , m (matrix column). Using this notation, the relationship between C i and C j can be defined as C 1 T ∗ C j (matrix multiplication) where i and j are from three different sets (ICT tools, online learning approaches, fields of study).

The descriptive overview presented in Table 1 shows the main characteristics of online learning and COVID-19 research in the higher education context. This research area covers a total of 8,303 documents (of which 7,922 (95%) have the full text in English) published in 2,447 sources between January 2020 and March 2022. Slightly less than half (46%) of these documents have at least one citation, while a relatively small number (15%) were written by a single author. The average number of references per document in this research area is 31.39, which is below the general scientific area of Educational Research (44.00) ( Patience et al., 2017 ), suggesting that online learning research during the COVID-19 pandemic is grounded on fewer existing studies than general research. Finally, 3.50 citations per document can be observed for this research area. Due to the potential benefits of online learning, especially when combined with the traditional learning approaches and hence the development of the blended learning environment, this research is expected to further develop and be extended in the ensuing years ( Fauzi, 2022 ). Further, upon analyzing the documents, it is evident that the average year of references is 2014.03, with an h-index of 60 (indicating at least 60 papers with 60 or more citations each) and a g-index of 94 (denoting that the top 94 publications have accumulated citations equal to or greater than the square of 94). Finally, it was found that within the examined dataset, a total of 1,334 documents (16%) have achieved a minimum of 5 citations (C5), while 691 documents (8%) have attained at least 10 citations (C10), 302 documents (4%) have obtained a minimum of 20 citations (C20), 79 documents (1%) have acquired at least 50 citations (C50), and 31 documents (0.4%) have obtained more than 100 citations (C100).

Table 1 . Descriptive overview of online learning and COVID-19 research (2020–2022).

The most relevant (top-10) highly cited documents in online learning and COVID-19 research in the context of higher education are shown in Table 2 . The overview of the most relevant documents reveals several important topics that were intensively discussed. The first most relevant topic concerns ICT. The COVID-19 pandemic has created significant challenges for higher education, especially for medical and surgical education, which requires personal attendance in clinical activities and labs. Accordingly, several innovative ICT tools (i.e., videoconferencing, social media, and telemedicine) and online learning approaches (i.e., flipped classroom or blended learning and virtual learning) were proposed to address this challenge. It is also stressed that by using appropriately established ICT solutions, online learning can lead to more sustainable education ( Adedoyin and Soykan, 2020 ; Chick et al., 2020 ; Dedeilia et al., 2020 ).

Table 2 . Most relevant documents in online learning and COVID-19 research (2020–2022).

The next top-cited topic relates to pedagogy. The disruption of education around the world due to the COVID-19 pandemic required teachers to possess specific pedagogical content knowledge related to designing and organizing better learning experiences with digital technologies. At the same time, challenges for online assessment and post-pandemic pedagogy are also highlighted ( García Peñalvo et al., 2020 ; Iyer et al., 2020 ; Murphy, 2020 ; Rapanta et al., 2020 ). Finally, life and work is another of the most cited topics. Namely, the COVID-19 pandemic has considerably reshaped education and other aspects of life and work, often also through the perspective of mental health or emotional well-being ( Dwivedi et al., 2020 ; Kapasia et al., 2020 ; Aristovnik et al., 2020a ).

Furthermore, it is noteworthy that all of the highly cited documents were published in 2020. However, it is also evident that there are notable and highly relevant publications that emerged in the second year of the COVID-19 pandemic. Accordingly, there are two documents with a minimum of 100 citations published in 2021. In the COVID-19 pandemic context, Watermeyer et al. (2021) , with 148 citations, examined the implications of digital disruption in universities within the United Kingdom, highlighting the challenges and opportunities arising from the emergency shift to online learning. Meanwhile, Pokhrel and Chhetri (2021) conducted a literature review to assess the impact of the COVID-19 pandemic on teaching and learning.

The scientific production across countries and sources is presented in terms of the number of documents and citations, whereby additional information is provided by a circle’s size, revealing the h-index as a measure of the scientific impact ( Harzing and Van Der Wal, 2009 ) and by its color, presenting the time dimension in scientific production. The most relevant (top-10) highly cited countries in online learning and COVID-19 research are shown in Figure 2 . While the United States of America stands out among all countries, the United Kingdom, China and India have a relatively large number of documents and citations. The findings are similar to those of other bibliometric studies on this topic ( Saqr et al., 2023 ).

Figure 2 . Most relevant countries in online learning and COVID-19 research (2020–2022). Own elaboration based on the Scopus database.

The most relevant (top-10) highly cited sources in online learning and COVID-19 research in the context of higher education are presented in Figure 3 . Despite conference proceedings being prominent in terms of the relatively high number of documents, the most prominent journals, considering the number of citations, are Journal of Chemical Education, with the highest number of citations as well as documents, followed by Sustainability, International Journal of Environmental Research and Public Health, and Education Sciences. More specifically, the most relevant journals address different topics. First, Journal of Chemical Education covers the attempts, successes and failures of distance learning during the COVID-19 pandemic in chemistry education. It covers various topics, including the development of at-home practical activities ( Schultz et al., 2020 ), student engagement and learning ( Perets et al., 2020 ), online assessments ( Nguyen et al., 2020 ) and virtual reality labs ( Williams et al., 2021 ). Further, Sustainability is focused on student and teacher perceptions of e-learning and related challenges ( Khan et al., 2020 ; Aristovnik et al., 2020a ) and sustainability in education during the COVID-19 pandemic ( Sobaih et al., 2020 ) to improve online learning and sustain higher education during uncertain times. Further, the International Journal of Environmental Research and Public Health covers various topics like the health and psychological implications of the COVID-19 pandemic ( Sundarasen et al., 2020 ), including well-being and changes in behavior and habits. Finally, Education Sciences publishes some general research on the challenges and opportunities for online learning ( Almazova et al., 2020 ), including student and teacher experiences ( García-Alberti et al., 2021 ; Müller et al., 2021 ).

Figure 3 . Most relevant sources in online learning and COVID-19 research (2020–2022). Own elaboration based on the Scopus database.

The keyword co-occurrence network is presented in Figure 4 . Note that the nodes indicate keywords and the links the relations of co-occurrence between them. The node size is proportional to the number of keyword occurrences, showing the research intensity (node degree), while the link width is proportional to the co-occurrences between keywords (edge weight). In addition, the node color indicates the cluster to which a particular keyword belongs ( Wang et al., 2020 ; Ravšelj et al., 2022 ). The keyword co-occurrence analysis reveals five research hotspots in online learning in higher education research during the COVID-19 pandemic. These are ICT and pedagogy (red cluster), technology-enhanced education (green cluster), mental health and well-being (blue cluster), student experience (yellow cluster) and curriculum and professional development (purple cluster).

Figure 4 . Keyword co-occurrence network in online learning and COVID-19 research (2020–2022). Own elaboration based on the Scopus database.

A detailed synopsis of the research hotspots, including representative (the most frequent) keywords and documents (with several representative keywords), is presented in Table 3 . The first research hotspot highlights the relevance of ICT and pedagogy in higher education during the COVID-19 pandemic. The most representative documents looked at the quality of online learning mechanisms ( Gritsova and Tissen, 2021 ), active learning activities ( Yan et al., 2021 ) and the role of e-learning departments in controlling the quality of academic processes ( Hamdan et al., 2021 ). The second research hotspot refers to technology-enhanced education from different perspectives, such as opportunities to incorporate technological and curricular innovations ( Shapiro and Reza, 2021 ), the adoption of different virtual experiences such as telehealth and virtual learning ( Kahwash et al., 2021 ), and the utilization of social media to reach higher education students ( Leighton et al., 2021 ). The third research hotspot emphasizes the problem of mental health and well-being issues that became a prevalent topic of discussion during the COVID-19 pandemic. Namely, several studies showed an increase in depression, anxiety and stress levels among higher education students in response to the COVID-19 pandemic ( Abu Kwaik et al., 2021 ; Keskin, 2021 ; Yaghi, 2022 ). The fourth cluster is about student experience during the COVID-19 pandemic with specific focus on the between interaction and online learning satisfaction ( Bawa'aneh, 2021 ; Bismala and Manurung, 2021 ; She et al., 2021 ). The fifth research hotspot underscores the relevance of curriculum and professional development. Several studies described the ways in which courses were adapted to online learning during the COVID-19 pandemic as well as the related challenges and strategies ( Chen et al., 2020 ; Gonzalez and Knecht, 2020 ; Rhile, 2020 ).

Table 3 . Research hotspots based on the author keyword co-occurrence network in online learning and COVID-19 research (2020–2022).

Finally, the three-field plot analysis of the relationship between the main concepts (i.e., ICT tools, online learning approaches, fields of study) is presented in a Sankey diagram shown in Figure 5 . The size of a rectangle corresponds to the number of documents for each theme, while the edge width reflects the inclusion index for connected themes ( Wang et al., 2020 ; Ravšelj et al., 2022 ). These three concepts have been proven to be relevant in the context of online learning. Namely, ICT tools are a precondition for delivering course content through different online learning approaches, while the choice of online learning approaches may depend on the field of study ( Ferri et al., 2020 ). During the COVID-19 pandemic, most attention was devoted to exploring e-learning (a combination of asynchronous and synchronous learning), distance learning (pre-recorded online lectures), followed by virtual learning (real-time online lectures). Since all these online learning approaches limit physical contact between teachers and students, they have been referred to as emergency remote learning approaches ( Hodges et al., 2020 ; Fauzi, 2022 ; Fuchs, 2022 ), while other online learning approaches (computer-based learning, blended learning, m-learning) do not necessarily take place in an online learning environment. The emergency remote learning approaches were primarily supported by several ICT tools, particularly by social media (e.g., Facebook), gamification/simulation and virtual reality (integration of game-like elements into online learning platforms, mobile applications, or virtual reality simulations), Zoom and other videoconferencing platforms, as well as telehealth (for educating health professionals). Regarding the fields of study, e-learning, distance learning and virtual learning were mostly addressed in the context of medical/health education, while computer-based learning (i.e., specific engineering software programs etc.) was examined in the context of engineering education. This implies that the specific requirements of a given field of study often guide the selection of the most suitable online learning approaches ( Fauzi, 2022 ).

Figure 5 . Three-field plot showing the network between ICT tools (left), online approaches (middle), and fields of study (right) (2020–2022). Own elaboration based on the Scopus database.

5. Conclusion

The presented bibliometric study provides several important insights arising from research into online learning during the COVID-19 pandemic. In this period, a large volume of scientific knowledge was produced in the context of education that considered a range of aspects ( Saqr et al., 2023 ). Therefore, a combination of selected bibliometric approaches was utilized to extract some general comprehensive outlines of the global research. The bibliometric analysis revealed the following.

As suggested by the descriptive overview of the state of Educational Research ( Patience et al., 2017 ), the research into online learning during the COVID-19 pandemic is characterized by greater cooperation between authors, which coincides with the general observation that (international) scientific collaboration grew significantly during the pandemic ( Duan and Xia, 2021 ). Further, online learning research during the COVID-19 pandemic is grounded on fewer studies than Educational Research ( Patience et al., 2017 ), which may be explained by the absence of COVID-19-related literature at the time these documents were published. Nevertheless, noting the potential benefits of online learning approaches also when the epidemiological conditions are favorable, this line of research is expected to further develop and be extended in the ensuing years ( Fauzi, 2022 ). The potential benefits refer especially to the development of a blended learning environment, which combines online and traditional learning approaches ( Rasheed et al., 2020 ). The overview of the most relevant documents revealed three topics that were intensively discussed in the academic community, i.e., ICT, pedagogy, and life and work. The COVID-19 pandemic highlighted the importance and role of reliable ICT infrastructure for ensuring effective pedagogy in the online environment, as was needed to prevent the spread of the virus and to protect public health. Apart from the devastating health consequences for those directly affected by the virus and the disrupted educational process, the COVID-19 pandemic also dramatically affected students’ social life and work ( Aristovnik et al., 2020a ). The educational community is increasingly interested in finding ways to respond to crises like the COVID-19 pandemic and develop effective pedagogical practices that assure high-quality and efficient education in the online learning environment ( Zhang et al., 2022 ).

The scientific production of online learning during the COVID-19 pandemic was geographically uneven. The greatest scientific production in terms of citations and number of documents can be observed in the United States, followed by the United Kingdom, China and India. Besides developed English-speaking countries, emerging Asian economies also seem to have played a crucial role in online learning research. Similar findings also emerged from other bibliometric studies on this topic ( Saqr et al., 2023 ). Moreover, despite conference proceedings being prominent in terms of the relatively high number of documents, the most prominent journals, considering the number of citations, are Journal of Chemical Education, Sustainability, International Journal of Environmental Research and Public Health and Education Sciences, indicating that online learning research at the time of the COVID-19 pandemic was primarily published in open-access journals, as already observed in other research ( Zhang et al., 2022 ).

The network analysis revealed five research hotspots in online learning research during the COVID-19 pandemic in the context of higher education: (1) ICT and pedagogy, focused on the quality of online learning mechanisms ( Gritsova and Tissen, 2021 ), active learning activities ( Yan et al., 2021 ) and the role of e-learning departments in controlling the quality of academic processes ( Hamdan et al., 2021 ); technology-enhanced education concentrated on opportunities to incorporate technological and curricular innovations ( Shapiro and Reza, 2021 ), the adoption of different virtual experiences such as telehealth and virtual learning ( Kahwash et al., 2021 ), and the utilization of social media to reach higher education students ( Leighton et al., 2021 ); (2) mental health and well-being issues facing higher education students, including depression, anxiety, and stress levels ( Abu Kwaik et al., 2021 ; Keskin, 2021 ; Yaghi, 2022 ); student experience with specific focus on the between interaction and online learning satisfaction ( Bawa'aneh, 2021 ; Bismala and Manurung, 2021 ; She et al., 2021 ) and (3) curriculum and professional development, focused on the ways in which courses were adapted to online learning during the COVID-19 pandemic as well as the related challenges and strategies ( Chen et al., 2020 ; Gonzalez and Knecht, 2020 ; Rhile, 2020 ).

Further, the COVID-19 pandemic led to the exploration of emergency remote learning approaches such as e-learning, distance learning and virtual learning, which are intended to limit physical contact between teachers and students. These approaches were chiefly supported by several ICT tools, including social media, gamification/simulation, virtual reality, videoconferencing platforms, and telehealth. While computer-based learning, blended learning and m-learning do not necessarily occur in an online learning environment, they may still be suitable for certain fields of study, especially in the post-COVID-19 pandemic period. This implies that the determination of which online learning approach is the most suitable is often guided by the specific requirements of a given field of study ( Fauzi, 2022 ).

Before generalizing these conclusions, it is important to note the limitations of the paper. First, the bibliometric analysis relied on documents indexed in the Scopus database, which might not cover the entire collection of research. Namely, documents that are published in journals indexed in other databases such as Web of Science, Education Research Index, Educational Resources Information Centre, etc. are not included in the analysis. However, to achieve the comparability of bibliometric metrics across documents, the bibliometric metrics are obtained from the single and, in general, broader Scopus database. Given the substantial overlap of documents across different databases of peer-reviewed literature, this limitation might not significantly affect the general observations on global research trends. Nevertheless, to check the robustness of the findings, it is still valuable to consider other bibliometric databases for future research. Second, the bibliometric analysis is conducted the bibliometric is based on a short time period (January 2020 – March 2022), which may also impact the metrics of documents published in closed-access (subscription-based) journals, placing them at a disadvantage compared to documents published in open-access journals. While it is not possible to overcome this limitation at present, conducting a bibliometric study with a longer time span would provide further time-dimensional insights. This would also be beneficial in terms of achieving better comparability between documents published in closed-access and open-access journals. Finally, despite the detailed search queries, some other relevant keywords may have been overlooked in the document search. Finally, the bibliometric method, as a method based on big data analysis, may miss certain highlights from the scientific literature that a systematic literature review would otherwise capture. Therefore it would be beneficial for future bibliometric studies also to incorporate a systematic literature review methodology, as the combined approach can provide a more comprehensive and nuanced understanding of the implications of the COVID-19 pandemic on online learning in higher education.

The bibliometric study provides some possible avenues for future research. First, in future bibliometric studies, it would be beneficial to conduct in-depth analyses of the relevant contexts that have emerged as highly significant in online learning during the pandemic. These include ICT and innovation, mental health and well-being, online learning and engagement, and curriculum and professional development. Examining these contexts more comprehensively can provide valuable insights into the specific dynamics and trends within each area, contributing to a deeper understanding of the implications of online learning during the pandemic. Second, it would be beneficial to conduct separate bibliometric analyses and comparisons to examine the differences between developed and developing countries. This approach can shed light on the unique research trends, contributions, and challenges faced by each group of countries in the context of online learning during the pandemic. This can provide a more nuanced understanding of the global landscape and identify potential areas for collaboration and knowledge sharing between developed and developing countries. Finally, it would be valuable to investigate the long-term impact of rapid publishing in open-access journals on the recognition and dissemination of scholarly findings in the field of online learning in higher education during the pandemic.

From the practical perspective, the COVID-19 pandemic has significantly disrupted higher education, but at the same time, it also accelerated the use of online learning tools in the educational process. Although the COVID-19 pandemic has gradually subsided over time, online learning approaches developed during this period continue to hold relevance and value for future education. Therefore, higher education institutions should prioritize leveraging ICT tools and innovative solutions in their educational delivery, which proved effective during the pandemic. Moreover, higher education institutions should also prioritize adapting appropriate online learning approaches and curricula to align with modern realities and the corresponding fields of study. This adaptation is crucial for enhancing student engagement and ensuring that educational programs remain relevant and responsive to the evolving needs of students in various disciplines.

The findings may help not only the scientific community in detecting research gaps in online learning research during the COVID-19 pandemic but also evidence-based policymaking by assisting in identifying appropriate educational practices in emergency circumstances. Specifically, the findings may help higher education policymakers to address the underlying shortcomings of the existing educational framework exposed by the COVID-19 pandemic and to design proactive mechanisms to deal effectively with such disruptions, thereby enabling them to create a more resilient and adaptable education system that can successfully navigate unforeseen challenges and ensure the continuity of quality higher education in the future.

Data availability statement

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

Author contributions

AA contributed to the design of the study. DR and LU assisted with the data identification, cleaning, and analysis. DR and KK wrote the manuscript in consultation with AA. All authors contributed to the manuscript’s revision and read and approved the submitted version.

This research and the APC were funded by the Slovenian Research Agency under grant numbers P5-0093 and Z5-4569.

Acknowledgments

The authors acknowledge the financial support from the Slovenian Research Agency (research core funding no. P5-0093 and project no. Z5-4569). A preliminary version of the paper was presented at the International Conference on Information, Communication Technologies in Education (ICICTE) in July 2022. The authors are grateful to colleagues who attended the presentation and provided interesting comments and suggestions. Further, they wish to thank the reviewers for their valuable suggestions and comments.

Conflict of interest

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

Publisher’s note

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

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Keywords: online learning, e-learning, higher education, bibliometrics, mapping, visualization, VOSviewer, COVID-19

Citation: Aristovnik A, Karampelas K, Umek L and Ravšelj D (2023) Impact of the COVID-19 pandemic on online learning in higher education: a bibliometric analysis. Front. Educ . 8:1225834. doi: 10.3389/feduc.2023.1225834

Received: 19 May 2023; Accepted: 14 July 2023; Published: 03 August 2023.

Reviewed by:

Copyright © 2023 Aristovnik, Karampelas, Umek and Ravšelj. 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: Aleksander Aristovnik, [email protected] ; Dejan Ravšelj, [email protected]

This article is part of the Research Topic

Increased Quality Education Through Cross-Campus Learning Environments

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After the crisis: Lessons from pandemic online learning

October 19, 2023

By Patricia Waldron

After the height of the COVID-19 pandemic, even as institutions had adapted to online learning, students belonging to underrepresented ethnic minority groups struggled to bounce back academically as compared with their non-minority classmates, according to new Cornell research.

Scholars in the Cornell Ann S. Bowers College of Computing and Information Science looked at student performance before, during, and after the transition to online learning due to the pandemic. While on average, grades returned to normal in 2021, the achievement gap between ethnic minority and non-minority groups widened, likely due to ongoing pandemic-related challenges. The researchers suggest that instructors monitor not only achievement but also engagement, to help identify struggling students.

“If we really want to provide equal opportunities for all students, and we want underrepresented minority students to do well, we really need to pay attention to their experiences and their perspectives and give them the tools to do better,” said Lilach Alon , formerly a postdoctoral researcher at Cornell and now a researcher and lecturer at Tel Hai College in Israel.

Alon is the lead author of “ From emergency to sustainable online learning: Changes and disparities in undergraduate course grades and experiences in the context of COVID-19 ,” which was published in the October issue of Computers & Education.

Amidst the crisis of the COVID-19 pandemic, the forced transition to online learning offered an opportunity for educators to innovate new teaching methods. In 2019, Alon and her co-authors had already been collecting data on student achievement and engagement in four large lecture courses at a large research university. When the pandemic struck and all classes moved online, they continued collecting data, first during the “emergency online learning” phase in spring 2020 and then in the “sustainable online learning” phase in 2021.

Pre-pandemic, students belonging to ethnic minority groups showed a small achievement gap in the courses, earning about a quarter of a grade lower, on average, than non-minority students. During lockdown in spring 2020, all types of students received higher grades due to a switch to pass/fail grading and institutional policies encouraging leniency and accommodation. After instructors had the chance to refine their online courses in 2021, students generally found the courses to be more active and engaging. However, a larger achievement gap emerged – almost three-quarters of a grade lower for students belonging to ethnic minority groups, which amounts to the difference between an A- and a B. 

While the exact reasons for this gap are unknown, previous studies suggest it is likely due to lack of resources – such as a quiet space and high-speed internet access – or that students belonging to ethnic minority groups more often must work or care for family members in addition to their coursework.

“It's easy to think that the pandemic is over and we're back to normal. But the research shows this is not necessarily true for all groups equally,” said René Kizilcec , assistant professor of information science and the paper’s senior author

From the study, the researchers drew several lessons that can be applied to improve the student experience in online courses today. Alon and her colleagues recommend that instructors give students multiple, smaller tasks during the semester to monitor achievement, instead of one big final, and that they give surveys to identify students who may need help. They also suggest that instructors structure courses to involve small group assignments and synchronous activities, where students are learning in real-time, to help students feel less disconnected.

Additionally, they said institutions need to offer more support and resources to instructors developing online courses. 

“It's not an easy task,”Alon said. “It's really hard, even for instructors who are experts in online learning.”

“In the academic literature, people say that hybrid online learning is the new reality of higher education,” Alon said. “If that's the case – and it seems to be the case in the U.S. – we need to really rethink pedagogy and student engagement.”

SeoYoon Sung, a former postdoctoral researcher at Cornell and Ji Yong Cho, a doctoral student in the field of information science also contributed to the study.

Patricia Waldron is a writer for the Cornell Ann S. Bowers College of Computing and Information Science.

The pandemic has had devastating impacts on learning. What will it take to help students catch up?

Subscribe to the brown center on education policy newsletter, megan kuhfeld , megan kuhfeld senior research scientist - nwea @megankuhfeld jim soland , jim soland assistant professor, school of education and human development - university of virginia, affiliated research fellow - nwea @jsoland karyn lewis , and karyn lewis director, center for school and student progress - nwea @karynlew emily morton emily morton research scientist - nwea @emily_r_morton.

March 3, 2022

As we reach the two-year mark of the initial wave of pandemic-induced school shutdowns, academic normalcy remains out of reach for many students, educators, and parents. In addition to surging COVID-19 cases at the end of 2021, schools have faced severe staff shortages , high rates of absenteeism and quarantines , and rolling school closures . Furthermore, students and educators continue to struggle with mental health challenges , higher rates of violence and misbehavior , and concerns about lost instructional time .

As we outline in our new research study released in January, the cumulative impact of the COVID-19 pandemic on students’ academic achievement has been large. We tracked changes in math and reading test scores across the first two years of the pandemic using data from 5.4 million U.S. students in grades 3-8. We focused on test scores from immediately before the pandemic (fall 2019), following the initial onset (fall 2020), and more than one year into pandemic disruptions (fall 2021).

Average fall 2021 math test scores in grades 3-8 were 0.20-0.27 standard deviations (SDs) lower relative to same-grade peers in fall 2019, while reading test scores were 0.09-0.18 SDs lower. This is a sizable drop. For context, the math drops are significantly larger than estimated impacts from other large-scale school disruptions, such as after Hurricane Katrina—math scores dropped 0.17 SDs in one year for New Orleans evacuees .

Even more concerning, test-score gaps between students in low-poverty and high-poverty elementary schools grew by approximately 20% in math (corresponding to 0.20 SDs) and 15% in reading (0.13 SDs), primarily during the 2020-21 school year. Further, achievement tended to drop more between fall 2020 and 2021 than between fall 2019 and 2020 (both overall and differentially by school poverty), indicating that disruptions to learning have continued to negatively impact students well past the initial hits following the spring 2020 school closures.

These numbers are alarming and potentially demoralizing, especially given the heroic efforts of students to learn and educators to teach in incredibly trying times. From our perspective, these test-score drops in no way indicate that these students represent a “ lost generation ” or that we should give up hope. Most of us have never lived through a pandemic, and there is so much we don’t know about students’ capacity for resiliency in these circumstances and what a timeline for recovery will look like. Nor are we suggesting that teachers are somehow at fault given the achievement drops that occurred between 2020 and 2021; rather, educators had difficult jobs before the pandemic, and now are contending with huge new challenges, many outside their control.

Clearly, however, there’s work to do. School districts and states are currently making important decisions about which interventions and strategies to implement to mitigate the learning declines during the last two years. Elementary and Secondary School Emergency Relief (ESSER) investments from the American Rescue Plan provided nearly $200 billion to public schools to spend on COVID-19-related needs. Of that sum, $22 billion is dedicated specifically to addressing learning loss using “evidence-based interventions” focused on the “ disproportionate impact of COVID-19 on underrepresented student subgroups. ” Reviews of district and state spending plans (see Future Ed , EduRecoveryHub , and RAND’s American School District Panel for more details) indicate that districts are spending their ESSER dollars designated for academic recovery on a wide variety of strategies, with summer learning, tutoring, after-school programs, and extended school-day and school-year initiatives rising to the top.

Comparing the negative impacts from learning disruptions to the positive impacts from interventions

To help contextualize the magnitude of the impacts of COVID-19, we situate test-score drops during the pandemic relative to the test-score gains associated with common interventions being employed by districts as part of pandemic recovery efforts. If we assume that such interventions will continue to be as successful in a COVID-19 school environment, can we expect that these strategies will be effective enough to help students catch up? To answer this question, we draw from recent reviews of research on high-dosage tutoring , summer learning programs , reductions in class size , and extending the school day (specifically for literacy instruction) . We report effect sizes for each intervention specific to a grade span and subject wherever possible (e.g., tutoring has been found to have larger effects in elementary math than in reading).

Figure 1 shows the standardized drops in math test scores between students testing in fall 2019 and fall 2021 (separately by elementary and middle school grades) relative to the average effect size of various educational interventions. The average effect size for math tutoring matches or exceeds the average COVID-19 score drop in math. Research on tutoring indicates that it often works best in younger grades, and when provided by a teacher rather than, say, a parent. Further, some of the tutoring programs that produce the biggest effects can be quite intensive (and likely expensive), including having full-time tutors supporting all students (not just those needing remediation) in one-on-one settings during the school day. Meanwhile, the average effect of reducing class size is negative but not significant, with high variability in the impact across different studies. Summer programs in math have been found to be effective (average effect size of .10 SDs), though these programs in isolation likely would not eliminate the COVID-19 test-score drops.

Figure 1: Math COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) Table 2; summer program results are pulled from Lynch et al (2021) Table 2; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span; Figles et al. and Lynch et al. report an overall effect size across elementary and middle grades. We were unable to find a rigorous study that reported effect sizes for extending the school day/year on math performance. Nictow et al. and Kraft & Falken (2021) also note large variations in tutoring effects depending on the type of tutor, with larger effects for teacher and paraprofessional tutoring programs than for nonprofessional and parent tutoring. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

Figure 2 displays a similar comparison using effect sizes from reading interventions. The average effect of tutoring programs on reading achievement is larger than the effects found for the other interventions, though summer reading programs and class size reduction both produced average effect sizes in the ballpark of the COVID-19 reading score drops.

Figure 2: Reading COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; extended-school-day results are from Figlio et al. (2018) Table 2; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) ; summer program results are pulled from Kim & Quinn (2013) Table 3; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: While Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span, Figlio et al. and Kim & Quinn report an overall effect size across elementary and middle grades. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

There are some limitations of drawing on research conducted prior to the pandemic to understand our ability to address the COVID-19 test-score drops. First, these studies were conducted under conditions that are very different from what schools currently face, and it is an open question whether the effectiveness of these interventions during the pandemic will be as consistent as they were before the pandemic. Second, we have little evidence and guidance about the efficacy of these interventions at the unprecedented scale that they are now being considered. For example, many school districts are expanding summer learning programs, but school districts have struggled to find staff interested in teaching summer school to meet the increased demand. Finally, given the widening test-score gaps between low- and high-poverty schools, it’s uncertain whether these interventions can actually combat the range of new challenges educators are facing in order to narrow these gaps. That is, students could catch up overall, yet the pandemic might still have lasting, negative effects on educational equality in this country.

Given that the current initiatives are unlikely to be implemented consistently across (and sometimes within) districts, timely feedback on the effects of initiatives and any needed adjustments will be crucial to districts’ success. The Road to COVID Recovery project and the National Student Support Accelerator are two such large-scale evaluation studies that aim to produce this type of evidence while providing resources for districts to track and evaluate their own programming. Additionally, a growing number of resources have been produced with recommendations on how to best implement recovery programs, including scaling up tutoring , summer learning programs , and expanded learning time .

Ultimately, there is much work to be done, and the challenges for students, educators, and parents are considerable. But this may be a moment when decades of educational reform, intervention, and research pay off. Relying on what we have learned could show the way forward.

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Transition to online learning during the COVID-19 pandemic

David john lemay.

a McGill University, Canada

Paul Bazelais

b John Abbott College, Canada

Tenzin Doleck

c Simon Fraser University, Canada

With the new pandemic reality that has beset us, teaching and learning activities have been thrust online. While much research has explored student perceptions of online and distance learning, none has had a social laboratory to study the effects of an enforced transition on student perceptions of online learning.

We surveyed students about their perceptions of online learning before and after the transition to online learning. As student perceptions are influenced by a range of contextual and institutional factors beyond the classroom, we expected that students would be overall sanguine to the project given that access, technology integration, and family and government support during the pandemic shutdown would mitigate the negative consequences.

Students overall reported positive academic outcomes. However, students reported increased stress and anxiety and difficulties concentrating, suggesting that the obstacles to fully online learning were not only technological and instructional challenges but also social and affective challenges of isolation and social distancing.

Our analysis shows that the specific context of the pandemic disrupted more than normal teaching and learning activities. Whereas students generally responded positively to the transition, their reluctance to continue learning online and the added stress and workload show the limits of this large scale social experiment. In addition to the technical and pedagogical dimensions, successfully supporting students in online learning environments will require that teachers and educational technologists attend to the social and affective dimensions of online learning as well.

1. Introduction

The novel coronavirus disease 2019 or Covid-19 ( Fauci, Lane, & Redfield, 2020 ) or SARS-CoV-2 ( Velavan & Meyer, 2020 ) pandemic outbreak has disrupted and changed how we socialize, work, and learn ( Brynjolfsson et al., 2020 ; Daniel, 2020 ; Haase, Cosco, Kervin, Riadi, & O'Connell, 2021 ; Gonzalez et al., 2020 ). Since the pandemic began, much human activity has transitioned online ( Donthu & Gustafsson, 2020 ; Kramer & Kramer, 2020 ). The profound effects of the pandemic are being especially felt in education ( Marinoni, Land, & Jensen, 2020 ; Schleicher, 2020 ; Stambough et al., 2020 ). For education, the pandemic is both a challenge ( Daniel, 2020 ) and an opportunity ( Azorín, 2020 ). Schools have closed to mitigate the spread of COVID-19 ( Pokhrel & Chhetri, 2021 ) and the Covid-19 pandemic has disrupted traditional models of learning ( Lemay and Doleck, 2020 ) and precipitated a move to online teaching and learning activities ( Lemay, Doleck, & Bazelais, 2021 ). In the wake of the pandemic, most institutions of higher education have had to reconsider ways of teaching and assessment ( García-Peñalvo, Corell, Abella-Garcí ).

Regarding teaching, this abrupt transition “has led to significantly intensified workloads for staff as they work to not only move teaching content and materials into the online space, but also become sufficiently adept in navigating the requisite software” ( Allen et al., 2020 , Allen et al., 2020 , p. 233). Likewise, students faced difficulties and challenges adapting to the abrupt and unplanned shift to online learning ( Baticulon et al., 2021 ). In fact, it is not surprising that we know little about students’ readiness for real-time online learning ( Tang et al., 2021 ). Previous research on online teaching and learning has generally shown that transitions are usually voluntary and/or planned; however, emergency transitions, such as the one brought upon by the Covid-19 pandemic, have relatively little body of knowledge ( García-Peñalvo et al., 2021 , García-Peñalvo et al., 2021 ; Iglesias-Pradas, Hernández-García, Chaparro-Peláez, & Prieto, 2021 ; Lemay et al., 2021 ). Considering this significant upheaval, we wanted to explore how these changes might influence student perceptions of online learning.

In this context it is important to understand student perceptions, to be able to develop successful interventions and correct deficits in learning. Examining student perceptions of online learning through the transition helps us to understand the limits and the potential of this mode of distance learning, and help to anticipate and adapt the effects of this sudden transition to online instruction.

2. Background

According to Kauffman (2015) , “students perceive online courses differently than traditional courses” (p.1). In a highly cited paper Song, Singleton, Hill, and Koh (2004) conducted a large-scale study of graduate student perceptions of online learning and found a mix of facilitating and discouraging factors. Students felt that course design was an important factor that distinguished successful from unsuccessful online learning experiences. A review by Nora and Snyder (2008) documented mixed evidence for improved learning outcomes for online learning over traditional classes as technical problems were a significant impediment, including user proficiency with technology but also time management and maintaining interest and motivation online. It is unclear to what extent a forced and precipitated transition to online learning might affect perceptions of online learning.

Some research suggests that students may perform differently across different modalities, and some may even perform better in online learning environments ( Cole et al., 2017 ; Fendler, Ruff, & Shrikhandle, 2018 ; Kurucay & Inan, 2017 ). Cole, Lennon, and Weber (2019) investigated the relationship between student perceptions of online learning practices, social belonging, and the learning climate, controlling for age and gender. They argue that successful online learning addresses the social dimension, to counter the absence and overcome the distance. They concluded that a successful fully online learning experience necessitates exploiting active learning strategies to create opportunities for connection and exchange. Indeed, there is research reporting that students are more appreciative of active learning strategies in online learning environments (Cole, Lennon, and Weber, 2019; Gómez-Rey, Barbera, & Fernández-Navarro, 2017 ; Koohang, Paliszkiewicz, Klein, & Nord, 2016 ).

It is acknowledged in the literature that online learning presents a learning environment that is distinct from face-to-face or classroom learning environments ( Bazelais, Doleck, & Lemay, 2018 ). Students generally have favorable perceptions of online learning although they have reservations around technological proficiency and adequate course designs ( Song et al., 2004 ). According to a recent review by Pokhrel and Chhetri (2021) , “broadly identified challenges with e-learning are accessibility, affordability, flexibility, learning pedagogy, life-long learning and educational policy” (p. 4). Anecdotally, many distance learning programs are successful and students have thrived when they have been adequately supported. However, the unequal social outcomes and deprivations of the pandemic means many students have been deprived of adequate educational support ( Flack, Walker, Bickerstaff, & Margetts, 2020 ). Many educationalists are lamenting the lost years of the pandemic and the lamentable effects for youth social development ( Allen, Mahamed, & Williams, 2020 ). Thus, it is not clear how an enforced transition to remote teaching might influence student perceptions of online learning.

3. Purpose of the study

We sought to explore how the pandemic influenced student perceptions of online learning.

3.1. Research question

We asked “How did the pandemic and the unprecedented institution wide transition to remote delivery of instruction influence student perceptions of online learning in terms of access, engagement and academic progress?

4.1. Design

We employed a cross-sectional survey-based design to gauge student perceptions of online learning before, during, and after the transition to remote instruction. We surveyed students at a college in Northeastern North America about their experience of the transition to online learning. Then, we compared and contrasted our findings with the empirical research to describe how student perceptions of online learning were influenced by the wholesale transition to online learning.

4.2. Procedure and participants

Participants completed an online survey during the middle months of 2020.Survey responses were collected online. Surveys were completed on a voluntary basis and were completely anonymous. The survey included questions related to the effects of transition to online learning during the COVID-19 pandemic.A total of N  = 149 students from a pre-university science program at an English Collège d'enseignement général et professionnel (CEGEP; for a review, see Bazelais, Lemay, & Doleck, 2016 ) participated in the study. A breakdown of the participant characteristics is provided in Table 1 .

General characteristics of the respondents.

4.3. Measures

We used items from an existing questionnaire ( Motz et al., 2020 ) adapted for the present context, focusing on student perceptions after the transition and assessing student reactions, and their perceptions of the impact of the transition on academic outcomes. We supplemented the Likert scale items with the following open-ended questions:

• 1. What one thing could the College have done to improve your experience after the transition to online instruction?
• 2. Reflecting on your transition to online instruction, what was the most negative outcome?
• 3. Reflecting on your transition to online instruction, what was the most positive outcome?
• 4. What one thing could your instructors have done to improve your experience after the transition to online instruction?
• 5. Is there anything else that you feel is important regarding your experience with the transition to online learning that you would like to share?

5. Analysis

5.1. background.

We summarized the results and calculated descriptive statistics. We analyzed tendencies to provide a holistic picture of student perceptions of online throughout the transition. Open-ended questions were summarized using thematic analysis to inductively group answers into categories.

Of the 149 students surveyed, 45 were employed prior to COVID-19, but then unemployed due to COVID-19. 28 continued to be employed throughout the Winter 2020 semester. 76 reported no employment during this period. This is unsurprising as nearly all the students surveyed were still living with their parents, save one student who reported living alone. Fig. 1 presents student living situations prior to the pandemic. Students reported difficulty finding adequate study space, due to interruptions from too many people, not having space, and too much noise. Fig. 4 shows that 95% had not taken an online course prior to the pandemic. When the Winter 2020 semester began, only nine students were taking courses fully online or in a blended learning situation (a hybrid model, mixing both online and face-to-face components). 90% of students were not taking any online courses. However, over a third were registered in at least one blended learning course. Please see Table 2 for the frequency of online, blended, and face-to-face courses reported by students. Virtually none of the students surveyed had taken fully online courses prior to the wholesale transition to remote instruction.

Student living situation prior to pandemic.

Winter course registration by delivery model prior to the transition to online learning.

* fully-online courses (100% online), hybrid courses (with some face-to-face and some online sessions), or face-to-face courses (with all sessions physically face-to-face).

Student experience of online learning prior to pandemic.

In Table 3 , we summarize student responses regarding levels of food or monetary insecurity in the earlier months of the pandemic. Whereas the pandemic was undeniably disruptive in everyone's lives, some were hit harder than others. Even for a relatively affluent population, food insecurity was an issue for a few. Signaling that despite governmental measures, some students were still facing hardships at home.

Student levels of food and money insecurity.

5.2. Technology details

In Table 4 , we summarize student responses concerning their access to technology and their preparation for online learning. Fig. 2 illustrates students’ principal mode of connecting to online learning resources. Over 85% had access to the necessary computer equipment and Internet access, though 10% struggled with Internet connectivity and 5% did not have adequate computer hardware. Whereas the majority felt sufficiently prepared, 15% were unprepared which increased to a worrying 40% when accounting for those who were mitigated. Hence, material issues of technology and access were not important factors for the majority of students surveyed.

Student access to technology and preparation for online learning.

Student internet connected device for online learning.

As can be seen in Fig. 2 , over 85% reported using their own laptop. Surprisingly, more than half reported working on their smartphone at least part of the time. Given that a smartphone has become a necessary part of life, for many it is their only connected device or their only device at all. If one has to choose between a smartphone or a laptop, the laptop is the greater luxury and often the loser in the cost benefit analysis. Still a few reported having to share equipment to participate in online learning activities. This is concerning since it is largely accepted as a matter of course that everyone is connected these days. Yet for a minority, access remains uncertain or variable. Virtually everyone surveyed reported having an internet connection at home though at least 10% reported poor Internet connectivity. In Fig. 3 , we observe that virtually all students connected using their residence's internet services.

Primary method of connecting to the internet.

5.3. Engagement

This section of the survey addresses how the shift to online instruction impacted student engagement at college and in their courses . As can be observed in Table 5 , Table 6 , the transition did lead to disaffection for many. Although most continued to identify as students, their academic goals became less important. 25% felt they were unsuccessful as a result of the transition to online learning even though 50% still found success in the transition.

Consequence of transition on student engagement in college and in class.

Consequence of transition on teaching and learning.

In Table 6 , students were asked to think about their specific experience of one of three physics courses from Winter 2020, after courses transitioned to online instruction. Overall, students reported increased workload and more challenging work but also increased teacher support, however they also reported poorer communication, less understanding of course goals and less interaction with their peers.

5.4. Reaction

The following section deals with student reaction to the pandemic and its effects on instruction. As we can clearly see, the pandemic provoked a lot of stress and anxiety in students (see Fig. 5 ). Paradoxically, some were happy or optimistic, perhaps driven by the “reset” that was discussed in the early days of the pandemic where we might reflect socially in our collective relationship to work and to each other. Whereas students struggled with self-discipline in online learning, they remained optimistic and motivated to achieve their goals and this did not appear dampened by the transition to online learning.

Student affect response to pandemic.

Although students experienced a higher incidence of stress or anxious emotions on average, a few were opportunistic and even excited at the prospect of moving to online learning as we can see in Table 7 . This is perhaps explainable by the fact that one third did not feel they had sufficient self-discipline to be successful at fully online learning. However, majority of the students reported feeling positive about their diligence. Indeed, that they had grown from the experience and that they expected to be rewarded for their hard work.

Student performance self-assessment of online learning during pandemic.

5.5. Standards outcomes

Table 8 describes how students felt the transition impacted the standards and outcomes of their courses. Overall, students did not perceive academic misconduct had increased. Although it was felt that teachers relaxed their standards somewhat, the majority believed that their grades accurately reflected their performance.

Student perceptions of academic standards and outcomes in online learning during pandemic.

5.6. Academic progress

In this section, we asked how students believed the transition to online instruction impacted their academic progress and future plans. Overall students appeared sanguine about the effects of the pandemic. As can be seen in Table 9 , many felt they were on pace to graduate. Most did not feel that they had been negatively impacted by the transition to online learning. Although one quarter did feel they had been held back in their academic progress due to the transition to online learning.

Student perceptions of academic progress in online learning during the pandemic.

5.7. Learning

In Table 10 we asked how students believed the transition to online instruction impacted their ability to learn. Students painted a somewhat bleaker picture of online learning, as many as one in three struggled with online discussions. While most appreciated the ability to replay videos, nearly two thirds found it very difficult to focus on online lectures.

Student Perceptions of Transition's Impact on their Ability to Learn.

5.8. Qualitative analysis

In addition to the Likert scale questions, students were also asked five open-ended questions. Answers were inductively coded into thematic groupings and are summarized in Table 11 , Table 12 , Table 13 , Table 14 , Table 15 below. Student open-ended answers are aligned with their responses to the Likert question items. Their answers manifest their concerns for organization, communication, and technological support for effective online learning. They especially call for standards in delivery of online instruction. Students would have liked teachers to be more understanding about the their needs and the obstacles and challenges posed by the pandemic. Students also highlighted the specific challenges of this wholesale transition to online learning: technological shortcomings or lack of support, and perceptions of increased workloads, less interactions, poorer communication, and more overall confusion. Students called for varying instruction and employing a greater range of instructional tools and strategies, and most importantly for increased interactions, whether by making more use of group chats, collectively worked problems on virtual whiteboards, and flipping the classroom by assigning lectures and using class time to discuss problems. Many students called for recording lectures so they could be consulted later.

What one thing could the College have done to improve your experience after the transition to online instruction?.

Reflecting on your transition to online instruction, what was the most negative outcome?.

Reflecting on your transition to online instruction, what was the most positive outcome?.

What one thing could your instructors have done to improve your experience after the transition to online instruction?.

Is there anything else that you feel is important regarding your experience with the transition to online learning that you would like to share?.

6. Discussion

This small survey reported on student perceptions of the transition to online learning at one college in Northeastern North America. Our results describe an overall successful transition in terms of student academic outcomes and instructional standards. However, this is far from saying that the transition was a runaway success. Students reported high levels of stress and anxiety, two thirds had difficulty concentrating in online learning, and few students were ready to continue studying online. While the outcomes could be worse, students will be happy to return to in-class instruction as lockdowns are lifted and institutions are reopened to the student population.

Our findings largely parallel the pre-pandemic literature on student perceptions of online learning. Students perceive both advantages and disadvantages to online learning ( Ebner & Gegenfurtner, 2019 ). What our results highlight however, is the emotional and psychological toll of fully remote learning. Social connection is sorely lacking after many months of enforced social distancing and isolation. This stresses the importance of the social and affective dimension of online learning ( León-Gómez, Gil-Fernández, & Calderón-Garrido, 2021 ). The relationship of the social and affective to student perceptions of online learning has not been explored in significant depth in the student perceptions literature. In light of the social effects of the pandemic on student life, our results stress the importance of online learning situations that create opportunities for connection and exchange ( Doleck, Bazelais, & Lemay, 2017 ; Kaufmann & Vallade, 2020 ). Our findings show that educators and educationalists cannot ignore the social and affective dimensions when planning and delivering online instructions for the simple well-being of many students who suffer in isolation. Our findings also support the community of inquiry notions of social and cognitive presence in successful online learning environments ( Akyol, Garrison, & Ozden, 2009 ).

In a recent study, Zheng, Yu, and Wu (2021) compared a blended learning situation where students interacted with their teacher over social media, and one face-to-face learning situation, finding that affective and cognitive learning are enhanced in blended learning and that the affective dimension appeared to mediate that effect though the two conditions did not significantly differ on grade point average, social presence, and academic self concept (though it significantly influenced cognitive learning). Studies persistently show that affective and cognitive presence are increased in blended learning ( Akyol et al., 2009 ; Shaber et al., 2010 ; Zheng et al., 2021 ). Zheng, Yu, and Wu's (2021) model is interesting as its shows quite starkly how affect and self-concept are tied to learning. These findings show that there are ways to transcend the isolation and create social and affective connections in online instruction.

Our present findings align with Zheng et al. (2021) as everyone's well-being was rudely tested by a year in social distancing and social isolation. The students in our study missed interacting with their peers in class and on campus. Many reported difficulty concentrating and heightened stress. And largely students reported increased workloads as the work shifted online. This contradicts the general discourse that assignments actually decreased. This disconnect can be understood from the perspective of distributed cognition and distributed learning, where the cognitive load is shared across a group of individuals. In this perspective the environment or the system provides affordances for activity. Work did not increase, as so much more of the cognitive load was redistributed on the individual and away from the group. Distributed cognition in an online or a face-to-face environment follow markedly different trajectories. Historically, it has been hard to reproduce online the flow of face-to-face classroom discourse where you can easily switch from one activity to another. Which is perhaps why the transition to online learning often took the form of a video conference call, with all the technical interruptions and the intrusions of private lives into the public sphere.

6.1. Limitations

As a cross-sectional survey study, we caution against any causal inferences. Moreover, the limited sample size recommends against generalizing findings to the larger population. At most, these findings are indicative of general tendencies. To show how our results might generalize, we highlight many points of convergence between our findings and the latest research on student perceptions of online learning. Our study could have been strengthened by the inclusion of other stakeholder perspectives and more in-depth qualitative analysis methods; however, we believe that our findings regarding student perceptions of online learning during the transition adds to the literature and helps to understand the encouraging and discouraging factors contributing to successful transitions to online learning.

6.2. Future directions

Future research should attempt to replicate our findings with other samples to compare results across populations to understand how institutional and contextual factors influence student perceptions of online learning. Researchers might employ more in-depth qualitative analysis methods to explore how instructional decisions interact with the social and affective dimensions and influence student receptibility to online learning.

No funding to report.

Compliance with ethical standards

Declaration of competing interest.

The authors declare that there is no conflict of interest.

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