essay about distance learning in times of pandemic

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

.chakra .wef-spn4bz{transition-property:var(--chakra-transition-property-common);transition-duration:var(--chakra-transition-duration-fast);transition-timing-function:var(--chakra-transition-easing-ease-out);cursor:pointer;text-decoration:none;outline:2px solid transparent;outline-offset:2px;color:inherit;}.chakra .wef-spn4bz:hover,.chakra .wef-spn4bz[data-hover]{text-decoration:underline;}.chakra .wef-spn4bz:focus-visible,.chakra .wef-spn4bz[data-focus-visible]{box-shadow:var(--chakra-shadows-outline);} Cathy Li

Farah lalani.

• 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.

Have you read?

The future of jobs report 2023, how to follow the growth summit 2023.

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.

Our education system is losing relevance. Here's how to unleash its potential

3 ways the coronavirus pandemic could reshape education, celebrities are helping the uk's schoolchildren learn during lockdown, don't miss any update on this topic.

Create a free account and access your personalized content collection with our latest publications and analyses.

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

Stay up to date:

Education, gender and work, related topics:.

.chakra .wef-1v7zi92{margin-top:var(--chakra-space-base);margin-bottom:var(--chakra-space-base);line-height:var(--chakra-lineHeights-base);font-size:var(--chakra-fontSizes-larger);}@media screen and (min-width: 56.5rem){.chakra .wef-1v7zi92{font-size:var(--chakra-fontSizes-large);}} Explore and monitor how .chakra .wef-ugz4zj{margin-top:var(--chakra-space-base);margin-bottom:var(--chakra-space-base);line-height:var(--chakra-lineHeights-base);font-size:var(--chakra-fontSizes-larger);color:var(--chakra-colors-yellow);}@media screen and (min-width: 56.5rem){.chakra .wef-ugz4zj{font-size:var(--chakra-fontSizes-large);}} Education, Gender and Work is affecting economies, industries and global issues

Forum stories .chakra .wef-dog8kz{margin-top:var(--chakra-space-base);margin-bottom:var(--chakra-space-base);line-height:var(--chakra-lineheights-base);font-weight:var(--chakra-fontweights-normal);} newsletter.

Bringing you weekly curated insights and analysis on the global issues that matter.

.chakra .wef-1dtnjt5{display:flex;align-items:center;flex-wrap:wrap;} More on Health and Healthcare Systems .chakra .wef-17xejub{flex:1;justify-self:stretch;align-self:stretch;} .chakra .wef-2sx2oi{display:inline-flex;vertical-align:middle;padding-inline-start:var(--chakra-space-1);padding-inline-end:var(--chakra-space-1);text-transform:uppercase;font-size:var(--chakra-fontSizes-smallest);border-radius:var(--chakra-radii-base);font-weight:var(--chakra-fontWeights-bold);background:none;box-shadow:var(--badge-shadow);align-items:center;line-height:var(--chakra-lineHeights-short);letter-spacing:1.25px;padding:var(--chakra-space-0);white-space:normal;color:var(--chakra-colors-greyLight);box-decoration-break:clone;-webkit-box-decoration-break:clone;}@media screen and (min-width: 37.5rem){.chakra .wef-2sx2oi{font-size:var(--chakra-fontSizes-smaller);}}@media screen and (min-width: 56.5rem){.chakra .wef-2sx2oi{font-size:var(--chakra-fontSizes-base);}} See all

The top global health stories from 2024

Shyam Bishen

December 17, 2024

5 ways generative AI could transform clinical trials

What is health equity and how can it help achieve universal health coverage?

Universal health coverage: a global problem with local solutions

Intelligent Clinical Trials: Using Generative AI to Fast-Track Therapeutic Innovations

Meet Liv, the AI helper supporting people recently diagnosed with dementia

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• 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

120k Accesses

186 Citations

124 Altmetric

Metrics details

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.

Similar content being viewed by others

Large socio-economic, geographic and demographic disparities exist in exposure to school closures

Elementary school teachers’ perspectives about learning during the COVID-19 pandemic

Uncovering Covid-19, distance learning, and educational inequality in rural areas of Pakistan and China: a situational analysis method

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.

Bambra, C., Riordan, R., Ford, J. & Matthews, F. The COVID-19 pandemic and health inequalities. J. Epidemiol. Commun. Health 74 , 964–968 (2020).

Google Scholar  

Johnson, P, Joyce, R & Platt, L. The IFS Deaton Review of Inequalities: A New Year’s Message (Institute for Fiscal Studies, 2021).

Education: from disruption to recovery. https://en.unesco.org/covid19/educationresponse (UNESCO, 2020).

Daszak, P. We are entering an era of pandemics—it will end only when we protect the rainforest. The Guardian (28 July 2020); https://www.theguardian.com/commentisfree/2020/jul/28/pandemic-era-rainforest-deforestation-exploitation-wildlife-disease

Dobson, A. P. et al. Ecology and economics for pandemic prevention. Science 369 , 379–381 (2020).

Article   CAS   PubMed   Google Scholar  

Harris, C., Straker, L. & Pollock, C. A socioeconomic related ‘digital divide’ exists in how, not if, young people use computers. PLoS ONE 12 , e0175011 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Zhang, M. Internet use that reproduces educational inequalities: evidence from big data. Comput. Educ. 86 , 212–223 (2015).

Article   Google Scholar  

Bourdieu, P. & Passeron, J. C. Reproduction in Education, Society and Culture (Sage, 1990).

Stephens, N. M., Fryberg, S. A., Markus, H. R., Johnson, C. S. & Covarrubias, R. Unseen disadvantage: how American universities’ focus on independence undermines the academic performance of first-generation college students. J. Pers. Soc. Psychol. 102 , 1178–1197 (2012).

Article   PubMed   Google Scholar  

Andrew, A. et al. Inequalities in children’s experiences of home learning during the COVID-19 lockdown in England. Fisc. Stud. 41 , 653–683 (2020).

Bol, T. Inequality in homeschooling during the Corona crisis in the Netherlands. First results from the LISS Panel. Preprint at SocArXiv https://doi.org/10.31235/osf.io/hf32q (2020).

Cullinane, C. & Montacute, R. COVID-19 and Social Mobility. Impact Brief #1: School Shutdown (The Sutton Trust, 2020).

Bandura, A. Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84 , 191–215 (1977).

Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G. & Hanson, J. Attitude, digital literacy and self efficacy: low-on effects for online learning behavior. Internet High. Educ. 29 , 91–97 (2016).

Robinson, L. et al. Digital inequalities 2.0: legacy inequalities in the information age. First Monday https://doi.org/10.5210/fm.v25i7.10842 (2020).

Cruz-Jesus, F., Vicente, M. R., Bacao, F. & Oliveira, T. The education-related digital divide: an analysis for the EU-28. Comput. Hum. Behav. 56 , 72–82 (2016).

Rice, R. E. & Haythornthwaite, C. In The Handbook of New Media (eds Lievrouw, L. A. & Livingstone S. M.), 92–113 (Sage, 2006).

Yates, S., Kirby, J. & Lockley, E. Digital media use: differences and inequalities in relation to class and age. Sociol. Res. Online 20 , 71–91 (2015).

Legleye, S. & Rolland, A. Une personne sur six n’utilise pas Internet, plus d’un usager sur trois manques de compétences numériques de base [One in six people do not use the Internet, more than one in three users lack basic digital skills] (INSEE Première, 2019).

Green, F. Schoolwork in lockdown: new evidence on the epidemic of educational poverty (LLAKES Centre, 2020); https://www.llakes.ac.uk/wp-content/uploads/2021/03/RP-67-Francis-Green-Research-Paper-combined-file.pdf

Vogels, E. Digital divide persists even as americans with lower incomes make gains in tech adoption (Pew Research Center, 2021); https://www.pewresearch.org/fact-tank/2021/06/22/digital-divide-persists-even-as-americans-with-lower-incomes-make-gains-in-tech-adoption/

McBurnie, C., Adam, T. & Kaye, T. Is there learning continuity during the COVID-19 pandemic? A synthesis of the emerging evidence. J. Learn. Develop. http://dspace.col.org/handle/11599/3720 (2020).

Baillet, J., Croutte, P. & Prieur, V. Baromètre du numérique 2019 [Digital barometer 2019] (Sourcing Crédoc, 2019).

Giraud, F., Bertrand, J., Court, M. & Nicaise, S. In Enfances de Classes. De l’inégalité Parmi les Enfants (ed. Lahire, B.) 933–952 (Seuil, 2019).

Ahamed, S. & Siddiqui, Z. Disparity in access to quality education and the digital divide (Ideas for India, 2020); https://www.ideasforindia.in/topics/macroeconomics/disparity-in-access-to-quality-education-and-the-digital-divide.html

Soomro, K. A., Kale, U., Curtis, R., Akcaoglu, M. & Bernstein, M. Digital divide among higher education faculty. Int. J. Educ. Tech. High. Ed. 17 , 21 (2020).

Meng, Q. & Li, M. New economy and ICT development in China. Inf. Econ. Policy 14 , 275–295 (2002).

Chinn, M. D. & Fairlie, R. W. The determinants of the global digital divide: a cross-country analysis of computer and internet penetration. Oxf. Econ. Pap. 59 , 16–44 (2006).

Lembani, R., Gunter, A., Breines, M. & Dalu, M. T. B. The same course, different access: the digital divide between urban and rural distance education students in South Africa. J. Geogr. High. Educ. 44 , 70–84 (2020).

Asadullah, N., Bhattacharjee, A., Tasnim, M. & Mumtahena, F. COVID-19, schooling, and learning (BRAC Institute of Governance & Development, 2020); https://bigd.bracu.ac.bd/wp-content/uploads/2020/06/COVID-19-Schooling-and-Learning_June-25-2020.pdf

Asanov, I., Flores, F., McKenzie, D., Mensmann, M. & Schulte, M. Remote-learning, time-use, and mental health of Ecuadorian high-school students during the COVID-19 quarantine. World Dev. 138 , 105225 (2021).

Kihui, N. Kenya: 80% of students missing virtual learning amid school closures—study. AllAfrica (18 May 2020); https://allafrica.com/stories/202005180774.html

Debenedetti, L., Hirji, S., Chabi, M. O. & Swigart, T. Prioritizing evidence-based responses in Burkina Faso to mitigate the economic effects of COVID-19: lessons from RECOVR (Innovations for Poverty Action, 2020); https://www.poverty-action.org/blog/prioritizing-evidence-based-responses-burkina-faso-mitigate-economic-effects-covid-19-lessons

Bosumtwi-Sam, C. & Kabay, S. Using data and evidence to inform school reopening in Ghana (Innovations for Poverty Action, 2020); https://www.poverty-action.org/blog/using-data-and-evidence-inform-school-reopening-ghana

Azubuike, O. B., Adegboye, O. & Quadri, H. Who gets to learn in a pandemic? Exploring the digital divide in remote learning during the COVID-19 pandemic in Nigeria. Int. J. Educ. Res. Open 2 , 100022 (2021).

Attewell, P. Comment: the first and second digital divides. Sociol. Educ. 74 , 252–259 (2001).

DiMaggio, P., Hargittai, E., Neuman, W. R. & Robinson, J. P. Social implications of the Internet. Annu. Rev. Sociol. 27 , 307–336 (2001).

Hargittai, E. Digital na(t)ives? Variation in Internet skills and uses among members of the ‘Net Generation’. Sociol. Inq. 80 , 92–113 (2010).

Iivari, N., Sharma, S. & Ventä-Olkkonen, L. Digital transformation of everyday life—how COVID-19 pandemic transformed the basic education of the young generation and why information management research should care? Int. J. Inform. Manag. 55 , 102183 (2020).

Wei, L. & Hindman, D. B. Does the digital divide matter more? Comparing the effects of new media and old media use on the education-based knowledge gap. Mass Commun. Soc. 14 , 216–235 (2011).

Octobre, S. & Berthomier, N. L’enfance des loisirs [The childhood of leisure]. Cult. Études 6 , 1–12 (2011).

Education at a glance 2015: OECD indicators (OECD, 2015); https://doi.org/10.1787/eag-2015-en

North, S., Snyder, I. & Bulfin, S. Digital tastes: social class and young people’s technology use. Inform. Commun. Soc. 11 , 895–911 (2008).

Robinson, L. & Schulz, J. Net time negotiations within the family. Inform. Commun. Soc. 16 , 542–560 (2013).

Bonfadelli, H. The Internet and knowledge gaps: a theoretical and empirical investigation. Eur. J. Commun. 17 , 65–84 (2002).

Drabowicz, T. Social theory of Internet use: corroboration or rejection among the digital natives? Correspondence analysis of adolescents in two societies. Comput. Educ. 105 , 57–67 (2017).

Nikken, P. & Jansz, J. Developing scales to measure parental mediation of young children’s Internet use. Learn. Media Technol. 39 , 250–266 (2014).

Danic, I., Fontar, B., Grimault-Leprince, A., Le Mentec, M. & David, O. Les espaces de construction des inégalités éducatives [The areas of construction of educational inequalities] (Presses Univ. de Rennes, 2019).

Goudeau, S. Comment l'école reproduit-elle les inégalités? [How does school reproduce inequalities?] (Univ. Grenoble Alpes Editions/Presses Univ. de Grenoble, 2020).

Bernstein, B. Class, Codes, and Control (Routledge, 1975).

Gaddis, S. M. The influence of habitus in the relationship between cultural capital and academic achievement. Soc. Sci. Res. 42 , 1–13 (2013).

Lamont, M. & Lareau, A. Cultural capital: allusions, gaps and glissandos in recent theoretical developments. Sociol. Theory 6 , 153–168 (1988).

Stephens, N. M., Markus, H. R. & Phillips, L. T. Social class culture cycles: how three gateway contexts shape selves and fuel inequality. Annu. Rev. Psychol. 65 , 611–634 (2014).

Lahire, B. Enfances de classe. De l’inégalité parmi les enfants [Social class childhood. Inequality among children] (Le Seuil, 2019).

Lareau, A. Unequal Childhoods: Class, Race, and Family Life (Univ. of California Press, 2003).

Bourdieu, P. La distinction. Critique sociale du jugement [Distinction: a social critique of the judgement of taste] (Éditions de Minuit, 1979).

Bradley, R. H., Corwyn, R. F., McAdoo, H. P. & Garcia Coll, C. The home environments of children in the United States part I: variations by age, ethnicity, and poverty status. Child Dev. 72 , 1844–1867 (2001).

Blevins‐Knabe, B. & Musun‐Miller, L. Number use at home by children and their parents and its relationship to early mathematical performance. Early Dev. Parent. 5 , 35–45 (1996).

LeFevre, J. A. et al. Pathays to mathematics: longitudinal predictors of performance. Child Dev. 81 , 1753–1767 (2010).

Lareau, A. Home Advantage. Social Class and Parental Intervention in Elementary Education (Falmer Press, 1989).

Guryan, J., Hurst, E. & Kearney, M. Parental education and parental time with children. J. Econ. Perspect. 22 , 23–46 (2008).

Hill, C. R. & Stafford, F. P. Allocation of time to preschool children and educational opportunity. J. Hum. Resour. 9 , 323–341 (1974).

Calarco, J. M. A Field Guide to Grad School: Uncovering the Hidden Curriculum (Princeton Univ. Press, 2020).

Daw, J. Parental income and the fruits of labor: variability in homework efficacy in secondary school. Res. Soc. Strat. Mobil. 30 , 246–264 (2012).

Rønning, M. Who benefits from homework assignments? Econ. Educ. Rev. 30 , 55–64 (2011).

Alexander, K. L., Entwisle, D. R. & Olson, L. S. Lasting consequences of the summer learning gap. Am. Sociol. Rev. 72 , 167–180 (2007).

Cooper, H., Nye, B., Charlton, K., Lindsay, J. & Greathouse, S. The effects of summer vacation on achievement test scores: a narrative and meta-analytic review. Rev. Educ. Res. 66 , 227–268 (1996).

Stewart, H., Watson, N. & Campbell, M. The cost of school holidays for children from low income families. Childhood 25 , 516–529 (2018).

Pensiero, N., Kelly, A. & Bokhove, C. Learning inequalities during the Covid-19 pandemic: how families cope with home-schooling (University of Southampton, 2020); https://doi.org/10.5258/SOTON/P0025

Stephens, N. M., Markus, H. R. & Townsend, S. S. Choice as an act of meaning: the case of social class. J. Pers. Soc. Psychol. 93 , 814–830 (2007).

Kraus, M. W., Piff, P. K. & Keltner, D. Social class, sense of control, and social explanation. J. Pers. Soc. Psychol. 97 , 992–1004 (2009).

Dittmann, A. G., Stephens, N. M. & Townsend, S. S. Achievement is not class-neutral: working together benefits pople from working-class contexts. J. Pers. Soc. Psychol. 119 , 517–539 (2020).

Zimmerman, B. J. Investigating self-regulation and motivation: historical background, methodological developments, and future prospects. Am. Educ. Res. J. 45 , 166–183 (2008).

Backer-Grøndahl, A., Nærde, A., Ulleberg, P. & Janson, H. Measuring effortful control using the children’s behavior questionnaire–very short form: modeling matters. J. Pers. Assess. 98 , 100–109 (2016).

Johnson, S. E., Richeson, J. A. & Finkel, E. J. Middle class and marginal? Socioeconomic status, stigma, and self-regulation at an elite university. J. Pers. Soc. Psychol. 100 , 838–852 (2011).

Størksen, I., Ellingsen, I. T., Wanless, S. B. & McClelland, M. M. The influence of parental socioeconomic background and gender on self-regulation among 5-year-old children in Norway. Early Educ. Dev. 26 , 663–684 (2015).

Brady, L. et al. 7 ways for teachers to truly connect with parents. Education Week (31 December 2020); https://www.edweek.org/leadership/opinion-7-ways-for-teachers-to-truly-connect-with-parents/2020/12

Montacute, R. Social mobility and Covid-19: implications of the Covid-19 crisis for educational inequality (Sutton Trust, 2020); https://dera.ioe.ac.uk/35323/2/COVID-19-and-Social-Mobility-1.pdf

Dorn, E., Hancock, B., Sarakatsannis, J. & Viruleg, E. COVID-19 and student learning in the United States: the hurt could last a lifetime (McKinsey & Company, 2020); https://www.mckinsey.com/industries/public-and-social-sector/our-insights/covid-19-and-student-learning-in-the-united-states-the-hurt-could-last-a-lifetime

Parolin, Z. & Lee, E. K. Large socio-economic, geographic and demographic disparities exist in exposure to school closures. Nat. Hum. Behav. 5 , 522–528 (2021).

Saavedra, J. A silent and unequal education crisis. And the seeds for its solution (World Bank, 2021); https://blogs.worldbank.org/education/silent-and-unequal-education-crisis-and-seeds-its-solution

Murray, B., Domina, T., Renzulli, L. & Boylan, R. Civil society goes to school: parent–teacher associations and the equality of educational opportunity. Russell Sage Found. J. Soc. Sci. 5 , 41–63 (2019).

Calarco, J. M. Avoiding us versus them: how schools’ dependence on privileged ‘helicopter’ parents influences enforcement of rules. Am. Sociol. Rev. 85 , 223–246 (2020).

Rist, R. Student social class and teacher expectations: the self-fulfilling prophecy in ghetto education. Harv. Educ. Rev. 40 , 411–451 (1970).

Tobisch, A. & Dresel, M. Negatively or positively biased? Dependencies of teachers’ judgments and expectations based on students’ ethnic and social backgrounds. Soc. Psychol. Educ. 20 , 731–752 (2017).

Brantlinger, E. Dividing Classes: How the Middle-class Negotiates and Rationalizes School Advantage (Routledge, 2003).

Calarco, J. M. ‘I need help!’ Social class and children’s help-seeking in elementary school. Am. Sociol. Rev. 76 , 862–882 (2011).

Calarco, J. M. The inconsistent curriculum: cultural tool kits and student interpretations of ambiguous expectations. Soc. Psychol. Quart. 77 , 185–209 (2014).

Goudeau, S. & Cimpian, A. How do young children explain differences in the classroom? Implications for achievement, motivation, and educational equity. Perspect. Psychol. Sci. 16 , 533–552 (2021).

Croizet, J. C., Goudeau, S., Marot, M. & Millet, M. How do educational contexts contribute to the social class achievement gap: documenting symbolic violence from a social psychological point of view. Curr. Opin. Psychol. 18 , 105–110 (2017).

Goudeau, S. & Croizet, J.-C. Hidden advantages and disadvantages of social class: how classroom settings reproduce social inequality by staging unfair comparison. Psychol. Sci. 28 , 162–170 (2017).

Kudrna, L., Furnham, A. & Swami, V. The influence of social class salience on self-assessed intelligence. Soc. Behav. Personal. 38 , 859–864 (2010).

Wiederkehr, V., Darnon, C., Chazal, S., Guimond, S. & Martinot, D. From social class to self-efficacy: internalization of low social status pupils’ school performance. Soc. Psychol. Educ. 18 , 769–784 (2015).

Jury, M., Smeding, A., Court, M. & Darnon, C. When first-generation students succeed at university: on the link between social class, academic performance, and performance-avoidance goals. Contemp. Educ. Psychol. 41 , 25–36 (2015).

Jury, M., Quiamzade, A., Darnon, C. & Mugny, G. Higher and lower status individuals’ performance goals: the role of hierarchy stability. Motiv. Sci. 5 , 52–65 (2019).

Autin, F. & Croizet, J.-C. Improving working memory efficiency by reframing metacognitive interpretation of task difficulty. J. Exp. Psychol. Gen. 141 , 610–618 (2012).

Schmader, T., Johns, M. & Forbes, C. An integrated process model of stereotype threat effects on performance. Psychol. Rev. 115 , 336–356 (2008).

Usher, E. L. & Pajares, F. Self-efficacy for self-regulated learning: a validation study. Educ. Psychol. Meas. 68 , 443–463 (2008).

Bruno, A., Jury, M., Toczek-Capelle, M.-C. & Darnon, C. Are performance-avoidance goals always deleterious for academic achievement in college? The moderating role of social class. Soc. Psychol. Educ. 22 , 539–555 (2019).

Holloway, S. D. et al. Parenting self-efficacy and parental involvement: mediators or moderators between socioeconomic status and children’s academic competence in Japan and Korea? Res. Hum. Dev. 13 , 258–272 (2016).

Tazouti, Y. & Jarlégan, A. The mediating effects of parental self-efficacy and parental involvement on the link between family socioeconomic status and children’s academic achievement. J. Fam. Stud. 25 , 250–266 (2019).

Andreu, S. et al. Évaluations 2020, repères CP, CE1: premiers résultats [2020 assessments, first and second grades benchmarks: first results] (Ministère de l’Éducation nationale, de la Jeunesse et des Sports, 2020); https://www.education.gouv.fr/evaluations-2020-reperes-cp-ce1-premiers-resultats-307122

Engzell, P., Frey, A. & Verhagen, M. D. Learning loss due to school closures during the COVID-19 pandemic. Proc. Natl Acad. Sci. USA 118 , e2022376118 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Maldonado, J. E. & De Witte, K. The effect of school closures on standardized student test outcomes (KU Leuven—Faculty of Economics and Business, 2020); https://limo.libis.be/primo-explore/fulldisplay?docid=LIRIAS3189074&context=L&vid=Lirias&search_scope=Lirias&tab=default_tab&lang=en_US

Domingue, B., Hough, H. J., Lang, D. & Yeatman, J. Changing patterns of growth in oral reading fluency during the COVID-19 pandemic (PACE, 2021); https://edpolicyinca.org/publications/changing-patterns-growth-oral-reading-fluency-during-covid-19-pandemic

Dorn, E., Hancock, B., Sarakatsannis, J. & Viruleg, E. COVID-19 and learning loss—disparities grow and students need help (McKinsey & Company, 2020); https://www.mckinsey.com/industries/public-and-social-sector/our-insights/covid-19-and-learning-loss-disparities-grow-and-students-need-help

Angrist, N. et al. Building back better to avert a learning catastrophe: estimating learning loss from COVID-19 school shutdowns in Africa and facilitating short-term and long-term learning recovery. Int. J. Educ. Dev. 84 , 102397 (2021).

Reddy, V., Soudien, C. & Winnaar, L. Disrupted learning during COVID-19: the impact of school closures on education outcomes in South Africa (The Conversation, 2020); https://theconversation.com/impact-of-school-closures-on-education-outcomes-in-south-africa-136889

Entwisle, D. R. & Alexander, K. L. Summer setback: race, poverty, school composition, and mathematics achievement in the first two years of school. Am. Sociol. Rev. 57 , 72–84 (1992).

Kieffer, M. J. Catching up or falling behind? Initial English proficiency, concentrated poverty, and the reading growth of language minority learners in the United States. J. Educ. Psychol. 100 , 851–868 (2008).

Calarco, J. M., Horn, I. & Chen, G. A. ‘You need to be more responsible’: how math homework operates as a status-reinforcing process in school. Preprint at SocArXiv https://doi.org/10.31235/osf.io/xf96q (2020).

Kaiper-Marquez, A. et al. On the fly: adapting quickly to emergency remote instruction in a family literacy program. Int. Rev. Educ. 66 , 1–23 (2020).

Barton, D. C. Impacts of the COVID‐19 pandemic on field instruction and remote teaching alternatives: results from a survey of instructors. Ecol. Evol. 10 , 12499–12507 (2020).

Article   PubMed Central   Google Scholar  

IJzerman, H. et al. Use caution when applying behavioural science to policy. Nat. Hum. Behav. 4 , 1092–1094 (2020).

Taylor, J. & Mallery, J. In person and online learning go together (Stanford Institute for Economic Policy Research, 2020); https://siepr.stanford.edu/research/publications/person-and-online-learning-go-together

Dietrichson, J., Bøg, M., Filges, T. & Klint Jørgensen, A. M. Academic interventions for elementary and middle school students with low socioeconomic status: a systematic review and meta-analysis. Rev. Educ. Res. 87 , 243–282 (2017).

Núñez, J. C. et al. Teachers’ feedback on homework, homework-related behaviors, and academic achievement. J. Educ. Res. 108 , 204–216 (2015).

Singh, R. et al. In Artificial Intelligence in Education (eds Biswas, G.et al.) 328–336 (Springer Berlin Heidelberg, 2011).

Harackiewicz, J. M., Rozek, C. S., Hulleman, C. S. & Hyde, J. S. Helping parents to motivate adolescents in mathematics and science: an experimental test of a utility-value intervention. Psychol. Sci. 23 , 899–906 (2012).

Jeynes, W. A meta-analysis of the efficacy of different types of parental involvement programs for urban students. Urban Educ. 47 , 706–742 (2012).

Mol, S. E., Bus, A. G., De Jong, M. T. & Smeets, D. J. Added value of dialogic parent–child book readings: a meta-analysis. Early Educ. Dev. 19 , 7–26 (2008).

Angrist, N., Bergman, P. & Matsheng, M. School’s out: experimental evidence on limiting learning loss using “low-tech” in a pandemic (National Bureau of Economic Research, 2021); https://www.nber.org/papers/w28205

Carlana, M. & La Ferrara, E. Apart but connected: online tutoring and student outcomes during the COVID-19 pandemic (Institute of Labor Economics, 2021); http://hdl.handle.net/10419/232846

Pagan, S. & Sénéchal, M. Involving parents in a summer book reading program to promote reading comprehension, fluency, and vocabulary in grade 3 and grade 5 children. Can. J. Educ. 37 , 1–31 (2014).

Sénéchal, M. & LeFevre, J. A. Parental involvement in the development of children’s reading skill: a five‐year longitudinal study. Child Dev. 73 , 445–460 (2002).

Starkey, P. & Klein, A. Fostering parental support for children’s mathematical development: an intervention with Head Start families. Early Educ. Dev. 11 , 659–680 (2000).

Buheji, M. et al. The extent of Covid-19 pandemic socio-economic impact on global poverty: a global integrative multidisciplinary review. Am. J. Econ. 10 , 213–224 (2020).

The world economy on a tightrope (OECD, 2020); http://www.oecd.org/economic-outlook/june-2020/

Martin, A., Markhvida, M., Hallegatte, S. & Walsh, B. Socio-economic impacts of COVID-19 on household consumption and poverty. Econ. Disasters Clim. Change 4 , 453–479 (2020).

Jetten, J., Mols, F. & Selvanathan, H. P. How economic inequality fuels the rise and persistence of the Yellow Vest movement. Int. Rev. Soc. Psychol. 33 , 2 (2020).

Wilkinson, R. G. & Pickett, K. E. Income inequality and social dysfunction. Annu. Rev. Sociol. 35 , 493–511 (2009).

Sommet, N., Morselli, D. & Spini, D. Income inequality affects the psychological health of only the people facing scarcity. Psychol. Sci. 29 , 1911–1921 (2018).

Hattie, J. Visible Learning: A Synthesis of over 800 Meta-analyses Relating to Achievement (Routledge, 2008).

Cooper, H., Charlton, K., Valentine, J. C., Muhlenbruck, L. & Borman, G. D. Making the most of summer school: a meta-analytic and narrative review. Monogr. Soc. Res. Child 65 , 1–127 (2000).

Heyns, B. Schooling and cognitive development: is there a season for learning? Child Dev. 58 , 1151–1160 (1987).

McCombs, J. S., Augustine, C. H. & Schwartz, H. L. Making Summer Count: How Summer Programs can Boost Children’s Learning (Rand Education, 2011).

Borman, G. D. & Dowling, N. M. Longitudinal achievement effects of multiyear summer school: evidence from the teach Baltimore randomized field trial. Educ. Eval. Policy 28 , 25–48 (2006).

Kim, J. S. & Quinn, D. M. The effects of summer reading on low-income children’s literacy achievement from kindergarten to grade 8: a meta-analysis of classroom and home interventions. Rev. Educ. Res. 83 , 386–431 (2013).

Download references

Acknowledgements

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.).

Author information

Authors and affiliations.

Université de Poitiers, CNRS, CeRCA, Centre de Recherches sur la Cognition et l’Apprentissage, Poitiers, France

Sébastien Goudeau & Camille Sanrey

Université Clermont Auvergne, CNRS, LAPSCO, Laboratoire de Psychologie Sociale et Cognitive, Clermont-Ferrand, France

Arnaud Stanczak & Céline Darnon

School of Psychology, Cardiff University, Cardiff, UK

Antony Manstead

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Sébastien Goudeau .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Daniele Checchi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 15 March 2021

Accepted : 06 September 2021

Published : 27 September 2021

Issue Date : October 2021

DOI : https://doi.org/10.1038/s41562-021-01212-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Digital nomadism from the perspective of places and mobilities: a literature review.

• Alberica Bozzi

European Transport Research Review (2024)

Inequality in pandemic effects on school track placement and the role of social and academic embeddedness

• Herman G. van de Werfhorst
• Dieuwke Zwier
• Carla Haelermans

npj Science of Learning (2024)

The roles that students’ ethnicity and achievement levels play in teachers’ choice of learning materials in online teaching: evidence from two experimental studies

• Sabine Schlag
• Sabine Glock

Instructional Science (2024)

It’s a problem, but not mine: Exploring bias-related message acceptance among teachers

• Lewis Doyle
• Matthew J. Easterbrook
• Peter R. Harris

Social Psychology of Education (2024)

Socioeconomic inequalities in psychosocial well-being among adolescents under the COVID-19 pandemic: a cross-regional comparative analysis in Hong Kong, mainland China, and the Netherlands

• Gary Ka-Ki Chung
• Xiaoting Liu
• Roger Yat-Nork Chung

Social Psychiatry and Psychiatric Epidemiology (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

An official website of the United States government

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Publications
• Account settings
• Advanced Search
• Journal List

Students’ experience of online learning during the COVID‐19 pandemic: A province‐wide survey study

Lixiang yan, alexander whitelock‐wainwright, quanlong guan, gangxin wen, dragan gašević, guanliang chen.

• Author information
• Article notes
• Copyright and License information

Correspondence , Guanliang Chen, Centre for Learning Analytics at Monash, Faculty of Information Technology, Monash University, 25 Exhibition Walk, Clayton VIC 3800, Australia. Email: [email protected]

Corresponding author.

Revised 2021 Mar 16; Received 2020 Nov 19; Accepted 2021 Mar 30; Issue date 2021 Sep.

This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

Online learning is currently adopted by educational institutions worldwide to provide students with ongoing education during the COVID‐19 pandemic. Even though online learning research has been advancing in uncovering student experiences in various settings (i.e., tertiary, adult, and professional education), very little progress has been achieved in understanding the experience of the K‐12 student population, especially when narrowed down to different school‐year segments (i.e., primary and secondary school students). This study explores how students at different stages of their K‐12 education reacted to the mandatory full‐time online learning during the COVID‐19 pandemic. For this purpose, we conducted a province‐wide survey study in which the online learning experience of 1,170,769 Chinese students was collected from the Guangdong Province of China. We performed cross‐tabulation and Chi‐square analysis to compare students’ online learning conditions, experiences, and expectations. Results from this survey study provide evidence that students’ online learning experiences are significantly different across school years. Foremost, policy implications were made to advise government authorises and schools on improving the delivery of online learning, and potential directions were identified for future research into K‐12 online learning.

Practitioner notes.

What is already known about this topic

Online learning has been widely adopted during the COVID‐19 pandemic to ensure the continuation of K‐12 education.

Student success in K‐12 online education is substantially lower than in conventional schools.

Students experienced various difficulties related to the delivery of online learning.

What this paper adds

Provide empirical evidence for the online learning experience of students in different school years.

Identify the different needs of students in primary, middle, and high school.

Identify the challenges of delivering online learning to students of different age.

Implications for practice and/or policy

Authority and schools need to provide sufficient technical support to students in online learning.

The delivery of online learning needs to be customised for students in different school years.

Keywords: e‐learning, learner attitudes/perceptions, primary education, questionnaire, secondary education

INTRODUCTION

The ongoing COVID‐19 pandemic poses significant challenges to the global education system. By July 2020, the UN Educational, Scientific and Cultural Organization (2020) reported nationwide school closure in 111 countries, affecting over 1.07 billion students, which is around 61% of the global student population. Traditional brick‐and‐mortar schools are forced to transform into full‐time virtual schools to provide students with ongoing education (Van Lancker & Parolin,  2020 ). Consequently, students must adapt to the transition from face‐to‐face learning to fully remote online learning, where synchronous video conferences, social media, and asynchronous discussion forums become their primary venues for knowledge construction and peer communication.

For K‐12 students, this sudden transition is problematic as they often lack prior online learning experience (Barbour & Reeves,  2009 ). Barbour and LaBonte ( 2017 ) estimated that even in countries where online learning is growing rapidly, such as USA and Canada, less than 10% of the K‐12 student population had prior experience with this format. Maladaptation to online learning could expose inexperienced students to various vulnerabilities, including decrements in academic performance (Molnar et al.,  2019 ), feeling of isolation (Song et al.,  2004 ), and lack of learning motivation (Muilenburg & Berge,  2005 ). Unfortunately, with confirmed cases continuing to rise each day, and new outbreaks occur on a global scale, full‐time online learning for most students could last longer than anticipated (World Health Organization,  2020 ). Even after the pandemic, the current mass adoption of online learning could have lasting impacts on the global education system, and potentially accelerate and expand the rapid growth of virtual schools on a global scale (Molnar et al.,  2019 ). Thus, understanding students' learning conditions and their experiences of online learning during the COVID pandemic becomes imperative.

Emerging evidence on students’ online learning experience during the COVID‐19 pandemic has identified several major concerns, including issues with internet connection (Agung et al.,  2020 ; Basuony et al.,  2020 ), problems with IT equipment (Bączek et al.,  2021 ; Niemi & Kousa,  2020 ), limited collaborative learning opportunities (Bączek et al.,  2021 ; Yates et al.,  2020 ), reduced learning motivation (Basuony et al.,  2020 ; Niemi & Kousa,  2020 ; Yates et al.,  2020 ), and increased learning burdens (Niemi & Kousa,  2020 ). Although these findings provided valuable insights about the issues students experienced during online learning, information about their learning conditions and future expectations were less mentioned. Such information could assist educational authorises and institutions to better comprehend students’ difficulties and potentially improve their online learning experience. Additionally, most of these recent studies were limited to higher education, except for Yates et al. ( 2020 ) and Niemi and Kousa’s ( 2020 ) studies on senior high school students. Empirical research targeting the full spectrum of K‐12students remain scarce. Therefore, to address these gaps, the current paper reports the findings of a large‐scale study that sought to explore K‐12 students’ online learning experience during the COVID‐19 pandemic in a provincial sample of over one million Chinese students. The findings of this study provide policy recommendations to educational institutions and authorities regarding the delivery of K‐12 online education.

LITERATURE REVIEW

Learning conditions and technologies.

Having stable access to the internet is critical to students’ learning experience during online learning. Berge ( 2005 ) expressed the concern of the divide in digital‐readiness, and the pedagogical approach between different countries could influence students’ online learning experience. Digital‐readiness is the availability and adoption of information technologies and infrastructures in a country. Western countries like America (3rd) scored significantly higher in digital‐readiness compared to Asian countries like China (54th; Cisco,  2019 ). Students from low digital‐readiness countries could experience additional technology‐related problems. Supporting evidence is emerging in recent studies conducted during the COVID‐19 pandemic. In Egypt's capital city, Basuony et al. ( 2020 ) found that only around 13.9%of the students experienced issues with their internet connection. Whereas more than two‐thirds of the students in rural Indonesia reported issues of unstable internet, insufficient internet data, and incompatible learning device (Agung et al.,  2020 ).

Another influential factor for K‐12 students to adequately adapt to online learning is the accessibility of appropriate technological devices, especially having access to a desktop or a laptop (Barbour et al., 2018 ). However, it is unlikely for most of the students to satisfy this requirement. Even in higher education, around 76% of students reported having incompatible devices for online learning and only 15% of students used laptop for online learning, whereas around 85% of them used smartphone (Agung et al.,  2020 ). It is very likely that K‐12 students also suffer from this availability issue as they depend on their parents to provide access to relevant learning devices.

Technical issues surrounding technological devices could also influence students’ experience in online learning. (Barbour & Reeves,  2009 ) argues that students need to have a high level of digital literacy to find and use relevant information and communicate with others through technological devices. Students lacking this ability could experience difficulties in online learning. Bączek et al. ( 2021 ) found that around 54% of the medical students experienced technical problems with IT equipment and this issue was more prevalent in students with lower years of tertiary education. Likewise, Niemi and Kousa ( 2020 ) also find that students in a Finish high school experienced increased amounts of technical problems during the examination period, which involved additional technical applications. These findings are concerning as young children and adolescent in primary and lower secondary school could be more vulnerable to these technical problems as they are less experienced with the technologies in online learning (Barbour & LaBonte,  2017 ). Therefore, it is essential to investigate the learning conditions and the related difficulties experienced by students in K‐12 education as the extend of effects on them remain underexplored.

Learning experience and interactions

Apart from the aforementioned issues, the extent of interaction and collaborative learning opportunities available in online learning could also influence students’ experience. The literature on online learning has long emphasised the role of effective interaction for the success of student learning. According to Muirhead and Juwah ( 2004 ), interaction is an event that can take the shape of any type of communication between two or subjects and objects. Specifically, the literature acknowledges the three typical forms of interactions (Moore,  1989 ): (i) student‐content, (ii) student‐student, and (iii) student‐teacher. Anderson ( 2003 ) posits, in the well‐known interaction equivalency theorem, learning experiences will not deteriorate if only one of the three interaction is of high quality, and the other two can be reduced or even eliminated. Quality interaction can be accomplished by across two dimensions: (i) structure—pedagogical means that guide student interaction with contents or other students and (ii) dialogue—communication that happens between students and teachers and among students. To be able to scale online learning and prevent the growth of teaching costs, the emphasise is typically on structure (i.e., pedagogy) that can promote effective student‐content and student‐student interaction. The role of technology and media is typically recognised as a way to amplify the effect of pedagogy (Lou et al.,  2006 ). Novel technological innovations—for example learning analytics‐based personalised feedback at scale (Pardo et al.,  2019 ) —can also empower teachers to promote their interaction with students.

Online education can lead to a sense of isolation, which can be detrimental to student success (McInnerney & Roberts,  2004 ). Therefore, integration of social interaction into pedagogy for online learning is essential, especially at the times when students do not actually know each other or have communication and collaboration skills underdeveloped (Garrison et al.,  2010 ; Gašević et al.,  2015 ). Unfortunately, existing evidence suggested that online learning delivery during the COVID‐19 pandemic often lacks interactivity and collaborative experiences (Bączek et al.,  2021 ; Yates et al.,  2020 ). Bączek et al., ( 2021 ) found that around half of the medical students reported reduced interaction with teachers, and only 4% of students think online learning classes are interactive. Likewise, Yates et al. ( 2020 )’s study in high school students also revealed that over half of the students preferred in‐class collaboration over online collaboration as they value the immediate support and the proximity to teachers and peers from in‐class interaction.

Learning expectations and age differentiation

Although these studies have provided valuable insights and stressed the need for more interactivity in online learning, K‐12 students in different school years could exhibit different expectations for the desired activities in online learning. Piaget's Cognitive Developmental Theory illustrated children's difficulties in understanding abstract and hypothetical concepts (Thomas,  2000 ). Primary school students will encounter many abstract concepts in their STEM education (Uttal & Cohen,  2012 ). In face‐to‐face learning, teachers provide constant guidance on students’ learning progress and can help them to understand difficult concepts. Unfortunately, the level of guidance significantly drops in online learning, and, in most cases, children have to face learning obstacles by themselves (Barbour,  2013 ). Additionally, lower primary school students may lack the metacognitive skills to use various online learning functions, maintain engagement in synchronous online learning, develop and execute self‐regulated learning plans, and engage in meaningful peer interactions during online learning (Barbour,  2013 ; Broadbent & Poon,  2015 ; Huffaker & Calvert, 2003; Wang et al.,  2013 ). Thus, understanding these younger students’ expectations is imperative as delivering online learning to them in the same way as a virtual high school could hinder their learning experiences. For students with more matured metacognition, their expectations of online learning could be substantially different from younger students. Niemi et al.’s study ( 2020 ) with students in a Finish high school have found that students often reported heavy workload and fatigue during online learning. These issues could cause anxiety and reduce students’ learning motivation, which would have negative consequences on their emotional well‐being and academic performance (Niemi & Kousa,  2020 ; Yates et al.,  2020 ), especially for senior students who are under the pressure of examinations. Consequently, their expectations of online learning could be orientated toward having additional learning support functions and materials. Likewise, they could also prefer having more opportunities for peer interactions as these interactions are beneficial to their emotional well‐being and learning performance (Gašević et al., 2013 ; Montague & Rinaldi, 2001 ). Therefore, it is imperative to investigate the differences between online learning expectations in students of different school years to suit their needs better.

Research questions

By building upon the aforementioned relevant works, this study aimed to contribute to the online learning literature with a comprehensive understanding of the online learning experience that K‐12 students had during the COVID‐19 pandemic period in China. Additionally, this study also aimed to provide a thorough discussion of what potential actions can be undertaken to improve online learning delivery. Formally, this study was guided by three research questions (RQs):

RQ1 . What learning conditions were experienced by students across 12 years of education during their online learning process in the pandemic period? RQ2 . What benefits and obstacles were perceived by students across 12 years of education when performing online learning? RQ3 . What expectations do students, across 12 years of education, have for future online learning practices ?

Participants

The total number of K‐12 students in the Guangdong Province of China is around 15 million. In China, students of Year 1–6, Year 7–9, and Year 10–12 are referred to as students of primary school, middle school, and high school, respectively. Typically, students in China start their study in primary school at the age of around six. At the end of their high‐school study, students have to take the National College Entrance Examination (NCEE; also known as Gaokao) to apply for tertiary education. The survey was administrated across the whole Guangdong Province, that is the survey was exposed to all of the 15 million K‐12 students, though it was not mandatory for those students to accomplish the survey. A total of 1,170,769 students completed the survey, which accounts for a response rate of 7.80%. After removing responses with missing values and responses submitted from the same IP address (duplicates), we had 1,048,575 valid responses, which accounts to about 7% of the total K‐12 students in the Guangdong Province. The number of students in different school years is shown in Figure  1 . Overall, students were evenly distributed across different school years, except for a smaller sample in students of Year 10–12.

The number of students in each school year

Survey design

The survey was designed collaboratively by multiple relevant parties. Firstly, three educational researchers working in colleges and universities and three educational practitioners working in the Department of Education in Guangdong Province were recruited to co‐design the survey. Then, the initial draft of the survey was sent to 30 teachers from different primary and secondary schools, whose feedback and suggestions were considered to improve the survey. The final survey consisted of a total of 20 questions, which, broadly, can be classified into four categories: demographic, behaviours, experiences, and expectations. Details are available in Appendix.

All K‐12 students in the Guangdong Province were made to have full‐time online learning from March 1, 2020 after the outbreak of COVID‐19 in January in China. A province‐level online learning platform was provided to all schools by the government. In addition to the learning platform, these schools can also use additional third‐party platforms to facilitate the teaching activities, for example WeChat and Dingding, which provide services similar to WhatsApp and Zoom. The main change for most teachers was that they had to shift the classroom‐based lectures to online lectures with the aid of web‐conferencing tools. Similarly, these teachers also needed to perform homework marking and have consultation sessions in an online manner.

The Department of Education in the Guangdong Province of China distributed the survey to all K‐12 schools in the province on March 21, 2020 and collected responses on March 26, 2020. Students could access and answer the survey anonymously by either scan the Quick Response code along with the survey or click the survey address link on their mobile device. The survey was administrated in a completely voluntary manner and no incentives were given to the participants. Ethical approval was granted by the Department of Education in the Guangdong Province. Parental approval was not required since the survey was entirely anonymous and facilitated by the regulating authority, which satisfies China's ethical process.

The original survey was in Chinese, which was later translated by two bilingual researchers and verified by an external translator who is certified by the Australian National Accreditation Authority of Translators and Interpreters. The original and translated survey questionnaires are available in Supporting Information. Given the limited space we have here and the fact that not every survey item is relevant to the RQs, the following items were chosen to answer the RQs: item Q3 (learning media) and Q11 (learning approaches) for RQ1, item Q13 (perceived obstacle) and Q19 (perceived benefits) for RQ2, and item Q19 (expected learning activities) for RQ3. Cross‐tabulation based approaches were used to analyse the collected data. To scrutinise whether the differences displayed by students of different school years were statistically significant, we performed Chi‐square tests and calculated the Cramer's V to assess the strengths of the association after chi‐square had determined significance.

For the analyses, students were segmented into four categories based on their school years, that is Year 1–3, Year 4–6, Year 7–9, and Year 10–12, to provide a clear understanding of the different experiences and needs that different students had for online learning. This segmentation was based on the educational structure of Chinese schools: elementary school (Year 1–6), middle school (Year 7–9), and high school (Year 10–12). Children in elementary school can further be segmented into junior (Year 1–3) or senior (Year 4–6) students because senior elementary students in China are facing more workloads compared to junior students due to the provincial Middle School Entry Examination at the end of Year 6.

Learning conditions—RQ1

Learning media.

The Chi‐square test showed significant association between school years and students’ reported usage of learning media, χ 2 (55, N  = 1,853,952) = 46,675.38, p  < 0.001. The Cramer's V is 0.07 ( df ∗ = 5), which indicates a small‐to‐medium effect according to Cohen’s ( 1988 ) guidelines. Based on Figure  2 , we observed that an average of up to 87.39% students used smartphones to perform online learning, while only 25.43% students used computer, which suggests that smartphones, with widespread availability in China (2020), have been adopted by students for online learning. As for the prevalence of the two media, we noticed that both smartphones ( χ 2 (3, N  = 1,048,575) = 9,395.05, p < 0.001, Cramer's V  = 0.10 ( df ∗ = 1)) and computers ( χ 2 (3, N  = 1,048,575) = 11,025.58, p <.001, Cramer's V  = 0.10 ( df ∗ = 1)) were more adopted by high‐school‐year (Year 7–12) than early‐school‐year students (Year 1–6), both with a small effect size. Besides, apparent discrepancies can be observed between the usages of TV and paper‐based materials across different school years, that is early‐school‐year students reported more TV usage ( χ 2 (3, N  = 1,048,575) = 19,505.08, p <.001), with a small‐to‐medium effect size, Cramer's V  = 0.14( df ∗ = 1). High‐school‐year students (especially Year 10–12) reported more usage of paper‐based materials ( χ 2 (3, N  = 1,048,575) = 23,401.64, p < 0.001), with a small‐to‐medium effect size, Cramer's V  = 0.15( df ∗ = 1).

Learning media used by students in online learning

Learning approaches

School years is also significantly associated with the different learning approaches students used to tackle difficult concepts during online learning, χ 2 (55, N  = 2,383,751) = 58,030.74, p < 0.001. The strength of this association is weak to moderate as shown by the Cramer's V (0.07, df ∗ = 5; Cohen,  1988 ). When encountering problems related to difficult concepts, students typically chose to “solve independently by searching online” or “rewatch recorded lectures” instead of consulting to their teachers or peers (Figure  3 ). This is probably because, compared to classroom‐based education, it is relatively less convenient and more challenging for students to seek help from others when performing online learning. Besides, compared to high‐school‐year students, early‐school‐year students (Year 1–6), reported much less use of “solve independently by searching online” ( χ 2 (3, N  = 1,048,575) = 48,100.15, p <.001), with a small‐to‐medium effect size, Cramer's V  = 0.21 ( df ∗ = 1). Also, among those approaches of seeking help from others, significantly more high‐school‐year students preferred “communicating with other students” than early‐school‐year students ( χ 2 (3, N  = 1,048,575) = 81,723.37, p < 0.001), with a medium effect size, Cramer's V  = 0.28 ( df ∗ = 1).

Learning approaches used by students in online learning

Perceived benefits and obstacles—RQ2

Perceived benefits.

The association between school years and perceived benefits in online learning is statistically significant, χ 2 (66, N  = 2,716,127) = 29,534.23, p  < 0.001, and the Cramer's V (0.04, df ∗ = 6) indicates a small effect (Cohen,  1988 ). Unsurprisingly, benefits brought by the convenience of online learning are widely recognised by students across all school years (Figure  4 ), that is up to 75% of students reported that it is “more convenient to review course content” and 54% said that they “can learn anytime and anywhere” . Besides, we noticed that about 50% of early‐school‐year students appreciated the “access to courses delivered by famous teachers” and 40%–47% of high‐school‐year students indicated that online learning is “helpful to develop self‐regulation and autonomy” .

Perceived benefits of online learning reported by students

Perceived obstacles

The Chi‐square test shows a significant association between school years and students’ perceived obstacles in online learning, χ 2 (77, N  = 2,699,003) = 31,987.56, p < 0.001. This association is relatively weak as shown by the Cramer's V (0.04, df ∗ = 7; Cohen,  1988 ). As shown in Figure  5 , the biggest obstacles encountered by up to 73% of students were the “eyestrain caused by long staring at screens” . Disengagement caused by nearby disturbance was reported by around 40% of students, especially those of Year 1–3 and 10–12. Technological‐wise, about 50% of students experienced poor Internet connection during their learning process, and around 20% of students reported the “confusion in setting up the platforms” across of school years.

Perceived obstacles of online learning reported by students

Expectations for future practices of online learning – RQ3

Online learning activities.

The association between school years and students’ expected online learning activities is significant, χ 2 (66, N  = 2,416,093) = 38,784.81, p < 0.001. The Cramer's V is 0.05 ( df ∗ = 6) which suggests a small effect (Cohen,  1988 ). As shown in Figure  6 , the most expected activity for future online learning is “real‐time interaction with teachers” (55%), followed by “online group discussion and collaboration” (38%). We also observed that more early‐school‐year students expect reflective activities, such as “regular online practice examinations” ( χ 2 (3, N  = 1,048,575) = 11,644.98, p < 0.001), with a small effect size, Cramer's V  = 0.11 ( df ∗ = 1). In contrast, more high‐school‐year students expect “intelligent recommendation system …” ( χ 2 (3, N  = 1,048,575) = 15,327.00, p < 0.001), with a small effect size, Cramer's V  = 0.12 ( df ∗ = 1).

Students’ expected online learning activities

Regarding students’ learning conditions, substantial differences were observed in learning media, family dependency, and learning approaches adopted in online learning between students in different school years. The finding of more computer and smartphone usage in high‐school‐year than early‐school‐year students can probably be explained by that, with the growing abilities in utilising these media as well as the educational systems and tools which run on these media, high‐school‐year students tend to make better use of these media for online learning practices. Whereas, the differences in paper‐based materials may imply that high‐school‐year students in China have to accomplish a substantial amount of exercise, assignments, and exam papers to prepare for the National College Entrance Examination (NCEE), whose delivery was not entirely digitised due to the sudden transition to online learning. Meanwhile, high‐school‐year students may also have preferred using paper‐based materials for exam practice, as eventually, they would take their NCEE in the paper format. Therefore, these substantial differences in students’ usage of learning media should be addressed by customising the delivery method of online learning for different school years.

Other than these between‐age differences in learning media, the prevalence of smartphone in online learning resonates with Agung et al.’s ( 2020 ) finding on the issues surrounding the availability of compatible learning device. The prevalence of smartphone in K‐12 students is potentially problematic as the majority of the online learning platform and content is designed for computer‐based learning (Berge,  2005 ; Molnar et al.,  2019 ). Whereas learning with smartphones has its own unique challenges. For example, Gikas and Grant ( 2013 ) discovered that students who learn with smartphone experienced frustration with the small screen‐size, especially when trying to type with the tiny keypad. Another challenge relates to the distraction of various social media applications. Although similar distractions exist in computer and web‐based social media, the level of popularity, especially in the young generation, are much higher in mobile‐based social media (Montag et al.,  2018 ). In particular, the message notification function in smartphones could disengage students from learning activities and allure them to social media applications (Gikas & Grant,  2013 ). Given these challenges of learning with smartphones, more research efforts should be devoted to analysing students’ online learning behaviour in the setting of mobile learning to accommodate their needs better.

The differences in learning approaches, once again, illustrated that early‐school‐year students have different needs compared to high‐school‐year students. In particular, the low usage of the independent learning methods in early‐school‐year students may reflect their inability to engage in independent learning. Besides, the differences in help seeking behaviours demonstrated the distinctive needs for communication and interaction between different students, that is early‐school‐year students have a strong reliance on teachers and high‐school‐year students, who are equipped with stronger communication ability, are more inclined to interact with their peers. This finding implies that the design of online learning platforms should take students’ different needs into account. Thus, customisation is urgently needed for the delivery of online learning to different school years.

In terms of the perceived benefits and challenges of online learning, our results resonate with several previous findings. In particular, the benefits of convenience are in line with the flexibility advantages of online learning, which were mentioned in prior works (Appana,  2008 ; Bączek et al.,  2021 ; Barbour,  2013 ; Basuony et al.,  2020 ; Harvey et al.,  2014 ). Early‐school‐year students’ higher appreciation in having “access to courses delivered by famous teachers” and lower appreciation in the independent learning skills developed through online learning are also in line with previous literature (Barbour,  2013 ; Harvey et al.,  2014 ; Oliver et al.,  2009 ). Again, these similar findings may indicate the strong reliance that early‐school‐year students place on teachers, while high‐school‐year students are more capable of adapting to online learning by developing independent learning skills.

Technology‐wise, students’ experience of poor internet connection and confusion in setting up online learning platforms are particularly concerning. The problem of poor internet connection corroborated the findings reported in prior studies (Agung et al.,  2020 ; Barbour,  2013 ; Basuony et al.,  2020 ; Berge,  2005 ; Rice,  2006 ), that is the access issue surrounded the digital divide as one of the main challenges of online learning. In the era of 4G and 5G networks, educational authorities and institutions that deliver online education could fall into the misconception of most students have a stable internet connection at home. The internet issue we observed is particularly vital to students’ online learning experience as most students prefer real‐time communications (Figure  6 ), which rely heavily on stable internet connection. Likewise, the finding of students’ confusion in technology is also consistent with prior studies (Bączek et al.,  2021 ; Muilenburg & Berge,  2005 ; Niemi & Kousa,  2020 ; Song et al.,  2004 ). Students who were unsuccessfully in setting up the online learning platforms could potentially experience declines in confidence and enthusiasm for online learning, which would cause a subsequent unpleasant learning experience. Therefore, both the readiness of internet infrastructure and student technical skills remain as the significant challenges for the mass‐adoption of online learning.

On the other hand, students’ experience of eyestrain from extended screen time provided empirical evidence to support Spitzer’s ( 2001 ) speculation about the potential ergonomic impact of online learning. This negative effect is potentially related to the prevalence of smartphone device and the limited screen size of these devices. This finding not only demonstrates the potential ergonomic issues that would be caused by smartphone‐based online learning but also resonates with the aforementioned necessity of different platforms and content designs for different students.

A less‐mentioned problem in previous studies on online learning experiences is the disengagement caused by nearby disturbance, especially in Year 1–3 and 10–12. It is likely that early‐school‐year students suffered from this problem because of their underdeveloped metacognitive skills to concentrate on online learning without teachers’ guidance. As for high‐school‐year students, the reasons behind their disengagement require further investigation in the future. Especially it would be worthwhile to scrutinise whether this type of disengagement is caused by the substantial amount of coursework they have to undertake and the subsequent a higher level of pressure and a lower level of concentration while learning.

Across age‐level differences are also apparent in terms of students’ expectations of online learning. Although, our results demonstrated students’ needs of gaining social interaction with others during online learning, findings (Bączek et al.,  2021 ; Harvey et al.,  2014 ; Kuo et al.,  2014 ; Liu & Cavanaugh,  2012 ; Yates et al.,  2020 ). This need manifested differently across school years, with early‐school‐year students preferring more teacher interactions and learning regulation support. Once again, this finding may imply that early‐school‐year students are inadequate in engaging with online learning without proper guidance from their teachers. Whereas, high‐school‐year students prefer more peer interactions and recommendation to learning resources. This expectation can probably be explained by the large amount of coursework exposed to them. Thus, high‐school‐year students need further guidance to help them better direct their learning efforts. These differences in students’ expectations for future practices could guide the customisation of online learning delivery.

Implications

As shown in our results, improving the delivery of online learning not only requires the efforts of policymakers but also depend on the actions of teachers and parents. The following sub‐sections will provide recommendations for relevant stakeholders and discuss their essential roles in supporting online education.

Technical support

The majority of the students has experienced technical problems during online learning, including the internet lagging and confusion in setting up the learning platforms. These problems with technology could impair students’ learning experience (Kauffman,  2015 ; Muilenburg & Berge,  2005 ). Educational authorities and schools should always provide a thorough guide and assistance for students who are experiencing technical problems with online learning platforms or other related tools. Early screening and detection could also assist schools and teachers to direct their efforts more effectively in helping students with low technology skills (Wilkinson et al.,  2010 ). A potential identification method involves distributing age‐specific surveys that assess students’ Information and Communication Technology (ICT) skills at the beginning of online learning. For example, there are empirical validated ICT surveys available for both primary (Aesaert et al.,  2014 ) and high school (Claro et al.,  2012 ) students.

For students who had problems with internet lagging, the delivery of online learning should provide options that require fewer data and bandwidth. Lecture recording is the existing option but fails to address students’ need for real‐time interaction (Clark et al.,  2015 ; Malik & Fatima,  2017 ). A potential alternative involves providing students with the option to learn with digital or physical textbooks and audio‐conferencing, instead of screen sharing and video‐conferencing. This approach significantly reduces the amount of data usage and lowers the requirement of bandwidth for students to engage in smooth online interactions (Cisco,  2018 ). It also requires little additional efforts from teachers as official textbooks are often available for each school year, and thus, they only need to guide students through the materials during audio‐conferencing. Educational authority can further support this approach by making digital textbooks available for teachers and students, especially those in financial hardship. However, the lack of visual and instructor presence could potentially reduce students’ attention, recall of information, and satisfaction in online learning (Wang & Antonenko,  2017 ). Therefore, further research is required to understand whether the combination of digital or physical textbooks and audio‐conferencing is appropriate for students with internet problems. Alternatively, suppose the local technological infrastructure is well developed. In that case, governments and schools can also collaborate with internet providers to issue data and bandwidth vouchers for students who are experiencing internet problems due to financial hardship.

For future adoption of online learning, policymakers should consider the readiness of the local internet infrastructure. This recommendation is particularly important for developing countries, like Bangladesh, where the majority of the students reported the lack of internet infrastructure (Ramij & Sultana,  2020 ). In such environments, online education may become infeasible, and alternative delivery method could be more appropriate, for example, the Telesecundaria program provides TV education for rural areas of Mexico (Calderoni,  1998 ).

Other than technical problems, choosing a suitable online learning platform is also vital for providing students with a better learning experience. Governments and schools should choose an online learning platform that is customised for smartphone‐based learning, as the majority of students could be using smartphones for online learning. This recommendation is highly relevant for situations where students are forced or involuntarily engaged in online learning, like during the COVID‐19 pandemic, as they might not have access to a personal computer (Molnar et al.,  2019 ).

Customisation of delivery methods

Customising the delivery of online learning for students in different school years is the theme that appeared consistently across our findings. This customisation process is vital for making online learning an opportunity for students to develop independent learning skills, which could help prepare them for tertiary education and lifelong learning. However, the pedagogical design of K‐12 online learning programs should be differentiated from adult‐orientated programs as these programs are designed for independent learners, which is rarely the case for students in K‐12 education (Barbour & Reeves,  2009 ).

For early‐school‐year students, especially Year 1–3 students, providing them with sufficient guidance from both teachers and parents should be the priority as these students often lack the ability to monitor and reflect on learning progress. In particular, these students would prefer more real‐time interaction with teachers, tutoring from parents, and regular online practice examinations. These forms of guidance could help early‐school‐year students to cope with involuntary online learning, and potentially enhance their experience in future online learning. It should be noted that, early‐school‐year students demonstrated interest in intelligent monitoring and feedback systems for learning. Additional research is required to understand whether these young children are capable of understanding and using learning analytics that relay information on their learning progress. Similarly, future research should also investigate whether young children can communicate effectively through digital tools as potential inability could hinder student learning in online group activities. Therefore, the design of online learning for early‐school‐year students should focus less on independent learning but ensuring that students are learning effective under the guidance of teachers and parents.

In contrast, group learning and peer interaction are essential for older children and adolescents. The delivery of online learning for these students should focus on providing them with more opportunities to communicate with each other and engage in collaborative learning. Potential methods to achieve this goal involve assigning or encouraging students to form study groups (Lee et al.,  2011 ), directing students to use social media for peer communication (Dabbagh & Kitsantas,  2012 ), and providing students with online group assignments (Bickle & Rucker,  2018 ).

Special attention should be paid to students enrolled in high schools. For high‐school‐year students, in particular, students in Year 10–12, we also recommend to provide them with sufficient access to paper‐based learning materials, such as revision booklet and practice exam papers, so they remain familiar with paper‐based examinations. This recommendation applies to any students who engage in online learning but has to take their final examination in paper format. It is also imperative to assist high‐school‐year students who are facing examinations to direct their learning efforts better. Teachers can fulfil this need by sharing useful learning resources on the learning management system, if it is available, or through social media groups. Alternatively, students are interested in intelligent recommendation systems for learning resources, which are emerging in the literature (Corbi & Solans,  2014 ; Shishehchi et al.,  2010 ). These systems could provide personalised recommendations based on a series of evaluation on learners’ knowledge. Although it is infeasible for situations where the transformation to online learning happened rapidly (i.e., during the COVID‐19 pandemic), policymakers can consider embedding such systems in future online education.

Limitations

The current findings are limited to primary and secondary Chinese students who were involuntarily engaged in online learning during the COVID‐19 pandemic. Despite the large sample size, the population may not be representative as participants are all from a single province. Also, information about the quality of online learning platforms, teaching contents, and pedagogy approaches were missing because of the large scale of our study. It is likely that the infrastructures of online learning in China, such as learning platforms, instructional designs, and teachers’ knowledge about online pedagogy, were underprepared for the sudden transition. Thus, our findings may not represent the experience of students who voluntarily participated in well‐prepared online learning programs, in particular, the virtual school programs in America and Canada (Barbour & LaBonte,  2017 ; Molnar et al.,  2019 ). Lastly, the survey was only evaluated and validated by teachers but not students. Therefore, students with the lowest reading comprehension levels might have a different understanding of the items’ meaning, especially terminologies that involve abstract contracts like self‐regulation and autonomy in item Q17.

In conclusion, we identified across‐year differences between primary and secondary school students’ online learning experience during the COVID‐19 pandemic. Several recommendations were made for the future practice and research of online learning in the K‐12 student population. First, educational authorities and schools should provide sufficient technical support to help students to overcome potential internet and technical problems, as well as choosing online learning platforms that have been customised for smartphones. Second, customising the online pedagogy design for students in different school years, in particular, focusing on providing sufficient guidance for young children, more online collaborative opportunity for older children and adolescent, and additional learning resource for senior students who are facing final examinations.

CONFLICT OF INTEREST

There is no potential conflict of interest in this study.

ETHICS STATEMENT

The data are collected by the Department of Education of the Guangdong Province who also has the authority to approve research studies in K12 education in the province.

Supporting information

Supplementary Material

ACKNOWLEDGEMENTS

This work is supported by the National Natural Science Foundation of China (62077028, 61877029), the Science and Technology Planning Project of Guangdong (2020B0909030005, 2020B1212030003, 2020ZDZX3013, 2019B1515120010, 2018KTSCX016, 2019A050510024), the Science and Technology Planning Project of Guangzhou (201902010041), and the Fundamental Research Funds for the Central Universities (21617408, 21619404).

SURVEY ITEMS

Yan, L , Whitelock‐Wainwright, A , Guan, Q , Wen, G , Gašević, D , & Chen, G . Students’ experience of online learning during the COVID‐19 pandemic: A province‐wide survey study. Br J Educ Technol. 2021;52:2038–2057. 10.1111/bjet.13102

DATA AVAILABILITY STATEMENT

The data is not openly available as it is restricted by the Chinese government.

• Aesaert, K. , Van Nijlen, D. , Vanderlinde, R. , & van Braak, J. (2014). Direct measures of digital information processing and communication skills in primary education: Using item response theory for the development and validation of an ICT competence scale. Computers & Education, 76, 168–181. 10.1016/j.compedu.2014.03.013 [ DOI ] [ Google Scholar ]
• Agung, A. S. N. , Surtikanti, M. W. , & Quinones, C. A. (2020). Students’ perception of online learning during COVID‐19 pandemic: A case study on the English students of STKIP Pamane Talino. SOSHUM: Jurnal Sosial Dan Humaniora, 10(2), 225–235. 10.31940/soshum.v10i2.1316 [ DOI ] [ Google Scholar ]
• Anderson, T. (2003). Getting the mix right again: An updated and theoretical rationale for interaction. The International Review of Research in Open and Distributed Learning, 4(2). 10.19173/irrodl.v4i2.149 [ DOI ] [ Google Scholar ]
• Appana, S. (2008). A review of benefits and limitations of online learning in the context of the student, the instructor and the tenured faculty. International Journal on E‐learning, 7(1), 5–22. [ Google Scholar ]
• Bączek, M. , Zagańczyk‐Bączek, M. , Szpringer, M. , Jaroszyński, A. , & Wożakowska‐Kapłon, B. (2021). Students’ perception of online learning during the COVID‐19 pandemic: A survey study of Polish medical students. Medicine, 100(7), e24821. 10.1097/MD.0000000000024821 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Barbour, M. K. (2013). The landscape of k‐12 online learning: Examining what is known. Handbook of Distance Education, 3, 574–593. [ Google Scholar ]
• Barbour, M. , Huerta, L. , & Miron, G. (2018). Virtual schools in the US: Case studies of policy, performance and research evidence. In Society for information technology & teacher education international conference (pp. 672–677). Association for the Advancement of Computing in Education (AACE). [ Google Scholar ]
• Barbour, M. K. , & LaBonte, R. (2017). State of the nation: K‐12 e‐learning in Canada, 2017 edition. http://k12sotn.ca/wp‐content/uploads/2018/02/StateNation17.pdf [ Google Scholar ]
• Barbour, M. K. , & Reeves, T. C. (2009). The reality of virtual schools: A review of the literature. Computers & Education, 52(2), 402–416. [ Google Scholar ]
• Basuony, M. A. K. , EmadEldeen, R. , Farghaly, M. , El‐Bassiouny, N. , & Mohamed, E. K. A. (2020). The factors affecting student satisfaction with online education during the COVID‐19 pandemic: An empirical study of an emerging Muslim country. Journal of Islamic Marketing. 10.1108/JIMA-09-2020-0301 [ DOI ] [ Google Scholar ]
• Berge, Z. L. (2005). Virtual schools: Planning for success. Teachers College Press, Columbia University. [ Google Scholar ]
• Bickle, M. C. , & Rucker, R. (2018). Student‐to‐student interaction: Humanizing the online classroom using technology and group assignments. Quarterly Review of Distance Education, 19(1), 1–56. [ Google Scholar ]
• Broadbent, J. , & Poon, W. L. (2015). Self‐regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. [ Google Scholar ]
• Calderoni, J. (1998). Telesecundaria: Using TV to bring education to rural Mexico (Tech. Rep.). The World Bank. [ Google Scholar ]
• Cisco . (2018). Bandwidth requirements for meetings with cisco Webex and collaboration meeting rooms white paper. http://dwz.date/dpbc [ Google Scholar ]
• Cisco . (2019). Cisco digital readiness 2019. https://www.cisco.com/c/m/en_us/about/corporate‐social‐responsibility/research‐resources/digital‐readiness‐index.html#/ (Library Catalog: www.cisco.com). [ Google Scholar ]
• Clark, C. , Strudler, N. , & Grove, K. (2015). Comparing asynchronous and synchronous video vs. text based discussions in an online teacher education course. Online Learning, 19(3), 48–69. [ Google Scholar ]
• Claro, M. , Preiss, D. D. , San Martín, E. , Jara, I. , Hinostroza, J. E. , Valenzuela, S. , Cortes, F. , & Nussbaum, M. (2012). Assessment of 21st century ICT skills in Chile: Test design and results from high school level students. Computers & Education, 59(3), 1042–1053. 10.1016/j.compedu.2012.04.004 [ DOI ] [ Google Scholar ]
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Routledge Academic. [ Google Scholar ]
• Corbi, A. , & Solans, D. B. (2014). Review of current student‐monitoring techniques used in elearning‐focused recommender systems and learning analytics: The experience API & LIME model case study. IJIMAI, 2(7), 44–52. [ Google Scholar ]
• Dabbagh, N. , & Kitsantas, A. (2012). Personal learning environments, social media, and self‐regulated learning: A natural formula for connecting formal and informal learning. The Internet and Higher Education, 15(1), 3–8. 10.1016/j.iheduc.2011.06.002 [ DOI ] [ Google Scholar ]
• Garrison, D. R. , Cleveland‐Innes, M. , & Fung, T. S. (2010). Exploring causal relationships among teaching, cognitive and social presence: Student perceptions of the community of inquiry framework. The Internet and Higher Education, 13(1–2), 31–36. 10.1016/j.iheduc.2009.10.002 [ DOI ] [ Google Scholar ]
• Gašević, D. , Adesope, O. , Joksimović, S. , & Kovanović, V. (2015). Externally‐facilitated regulation scaffolding and role assignment to develop cognitive presence in asynchronous online discussions. The Internet and Higher Education, 24, 53–65. 10.1016/j.iheduc.2014.09.006 [ DOI ] [ Google Scholar ]
• Gašević, D. , Zouaq, A. , & Janzen, R. (2013). “Choose your classmates, your GPA is at stake!” The association of cross‐class social ties and academic performance. American Behavioral Scientist, 57(10), 1460–1479. [ Google Scholar ]
• Gikas, J. , & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media. The Internet and Higher Education, 19, 18–26. [ Google Scholar ]
• Harvey, D. , Greer, D. , Basham, J. , & Hu, B. (2014). From the student perspective: Experiences of middle and high school students in online learning. American Journal of Distance Education, 28(1), 14–26. 10.1080/08923647.2014.868739 [ DOI ] [ Google Scholar ]
• Kauffman, H. (2015). A review of predictive factors of student success in and satisfaction with online learning. Research in Learning Technology, 23. 10.3402/rlt.v23.26507 [ DOI ] [ Google Scholar ]
• Kuo, Y.‐C. , Walker, A. E. , Belland, B. R. , Schroder, K. E. , & Kuo, Y.‐T. (2014). A case study of integrating interwise: Interaction, internet self‐efficacy, and satisfaction in synchronous online learning environments. International Review of Research in Open and Distributed Learning, 15(1), 161–181. 10.19173/irrodl.v15i1.1664 [ DOI ] [ Google Scholar ]
• Lee, S. J. , Srinivasan, S. , Trail, T. , Lewis, D. , & Lopez, S. (2011). Examining the relationship among student perception of support, course satisfaction, and learning outcomes in online learning. The Internet and Higher Education, 14(3), 158–163. 10.1016/j.iheduc.2011.04.001 [ DOI ] [ Google Scholar ]
• Liu, F. , & Cavanaugh, C. (2012). Factors influencing student academic performance in online high school algebra. Open Learning: The Journal of Open, Distance and e‐Learning, 27(2), 149–167. 10.1080/02680513.2012.678613 [ DOI ] [ Google Scholar ]
• Lou, Y. , Bernard, R. M. , & Abrami, P. C. (2006). Media and pedagogy in undergraduate distance education: A theory‐based meta‐analysis of empirical literature. Educational Technology Research and Development, 54(2), 141–176. 10.1007/s11423-006-8252-x [ DOI ] [ Google Scholar ]
• Malik, M. , & Fatima, G. (2017). E‐learning: Students’ perspectives about asynchronous and synchronous resources at higher education level. Bulletin of Education and Research, 39(2), 183–195. [ Google Scholar ]
• McInnerney, J. M. , & Roberts, T. S. (2004). Online learning: Social interaction and the creation of a sense of community. Journal of Educational Technology & Society, 7(3), 73–81. [ Google Scholar ]
• Molnar, A. , Miron, G. , Elgeberi, N. , Barbour, M. K. , Huerta, L. , Shafer, S. R. , & Rice, J. K. (2019). Virtual schools in the US 2019. National Education Policy Center. [ Google Scholar ]
• Montague, M. , & Rinaldi, C. (2001). Classroom dynamics and children at risk: A followup. Learning Disability Quarterly, 24(2), 75–83. [ Google Scholar ]
• Montag, C. , Becker, B. , & Gan, C. (2018). The multipurpose application Wechat: A review on recent research. Frontiers in Psychology, 9, 2247. 10.3389/fpsyg.2018.02247 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1–7. 10.1080/08923648909526659 [ DOI ] [ Google Scholar ]
• Muilenburg, L. Y. , & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study. Distance Education, 26(1), 29–48. 10.1080/01587910500081269 [ DOI ] [ Google Scholar ]
• Muirhead, B. , & Juwah, C. (2004). Interactivity in computer‐mediated college and university education: A recent review of the literature. Journal of Educational Technology & Society, 7(1), 12–20. [ Google Scholar ]
• Niemi, H. M. , & Kousa, P. (2020). A case study of students’ and teachers’ perceptions in a finnish high school during the COVID pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. 10.46328/ijtes.v4i4.167 [ DOI ] [ Google Scholar ]
• Oliver, K. , Osborne, J. , & Brady, K. (2009). What are secondary students’ expectations for teachers in virtual school environments? Distance Education, 30(1), 23–45. 10.1080/01587910902845923 [ DOI ] [ Google Scholar ]
• Pardo, A. , Jovanovic, J. , Dawson, S. , Gašević, D. , & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology, 50(1), 128–138. 10.1111/bjet.12592 [ DOI ] [ Google Scholar ]
• Ramij, M. , & Sultana, A. (2020). Preparedness of online classes in developing countries amid covid‐19 outbreak: A perspective from Bangladesh. Afrin, Preparedness of Online Classes in Developing Countries amid COVID‐19 Outbreak: A Perspective from Bangladesh (June 29, 2020) .
• Rice, K. L. (2006). A comprehensive look at distance education in the k–12 context. Journal of Research on Technology in Education, 38(4), 425–448. 10.1080/15391523.2006.10782468 [ DOI ] [ Google Scholar ]
• Shishehchi, S. , Banihashem, S. Y. , & Zin, N. A. M. (2010). A proposed semantic recommendation system for elearning: A rule and ontology based e‐learning recommendation system. In 2010 international symposium on information technology (Vol. 1, pp. 1–5).
• Song, L. , Singleton, E. S. , Hill, J. R. , & Koh, M. H. (2004). Improving online learning: Student perceptions of useful and challenging characteristics. The Internet and Higher Education, 7(1), 59–70. 10.1016/j.iheduc.2003.11.003 [ DOI ] [ Google Scholar ]
• Spitzer, D. R. (2001). Don’t forget the high‐touch with the high‐tech in distance learning. Educational Technology, 41(2), 51–55. [ Google Scholar ]
• Thomas, R. M. (2000). Comparing theories of child development. Wadsworth/Thomson Learning. United Nations Educational, Scientific and Cultural Organization. (2020, March). Education: From disruption to recovery . https://en.unesco.org/covid19/educationresponse (Library Catalog: en.unesco.org)
• Uttal, D. H. , & Cohen, C. A. (2012). Spatial thinking and stem education: When, why, and how? In Psychology of learning and motivation (Vol. 57, pp. 147–181). Elsevier. [ Google Scholar ]
• Van Lancker, W. , & Parolin, Z. (2020). Covid‐19, school closures, and child poverty: A social crisis in the making. The Lancet Public Health, 5(5), e243–e244. 10.1016/S2468-2667(20)30084-0 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Wang, C.‐H. , Shannon, D. M. , & Ross, M. E. (2013). Students’ characteristics, self‐regulated learning, technology self‐efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323. 10.1080/01587919.2013.835779 [ DOI ] [ Google Scholar ]
• Wang, J. , & Antonenko, P. D. (2017). Instructor presence in instructional video: Effects on visual attention, recall, and perceived learning. Computers in Human Behavior, 71, 79–89. 10.1016/j.chb.2017.01.049 [ DOI ] [ Google Scholar ]
• Wilkinson, A. , Roberts, J. , & While, A. E. (2010). Construction of an instrument to measure student information and communication technology skills, experience and attitudes to e‐learning. Computers in Human Behavior, 26(6), 1369–1376. 10.1016/j.chb.2010.04.010 [ DOI ] [ Google Scholar ]
• World Health Organization . (2020, July). Coronavirus disease 2019 (COVID‐19): Situation Report‐164 (Situation Report No. 164). https://www.who.int/docs/default‐source/coronaviruse/situation‐reports/20200702‐covid‐19‐sitrep‐164.pdf?sfvrsn$=$ac074f58$_$2
• Yates, A. , Starkey, L. , Egerton, B. , & Flueggen, F. (2020). High school students’ experience of online learning during Covid‐19: The influence of technology and pedagogy. Technology, Pedagogy and Education, 9, 1–15. 10.1080/1475939X.2020.1854337 [ DOI ] [ Google Scholar ]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data availability statement.

• View on publisher site
• PDF (1.6 MB)
• Collections

Similar articles

Cited by other articles, links to ncbi databases.

• Download .nbib .nbib
• Format: AMA APA MLA NLM

Add to Collections

• Share full article

Advertisement

Supported by

Why I’m Learning More With Distance Learning Than I Do in School

I’m 13 years old. I don’t miss the other kids who talk out of turn, disrespect teachers and hit one another.

By Veronique Mintz

Ms. Mintz is an eighth-grade student.

Talking out of turn. Destroying classroom materials. Disrespecting teachers. Blurting out answers during tests. Students pushing, kicking, hitting one another and even rolling on the ground. This is what happens in my school every single day.

You may think I’m joking, but I swear I’m not.

Based on my peers’ behavior, you might guess that I’m in second or fourth grade. But I’m actually about to enter high school in New York City, and, during my three years of middle school, these sorts of disruptions occurred repeatedly in any given 42-minute class period.

That’s why I’m in favor of the distance learning the New York City school system instituted when the coronavirus pandemic hit. If our schools use this experience to understand how to better support teachers in the classroom, then students will have a shot at learning more effectively when we return.

Let me explain why.

I have been doing distance learning since March 23 and find that I am learning more, and with greater ease, than when I attended regular classes. I can work at my own pace without being interrupted by disruptive students and teachers who seem unable to manage them.

Students unable or unwilling to control themselves steal valuable class time, often preventing their classmates from being prepared for tests and assessments. I have taken tests that included entire topics we never mastered, either because we were not able to get through the lesson or we couldn’t sufficiently focus.

I do not envy a middle-school teacher’s job. It’s far from easy to oversee 26 teenagers. And in my three years of middle school, I’ve encountered only a few teachers who had strong command of their classrooms — enforcing consistent rules, treating students fairly and earning their respect.

I go to a school that puts a big emphasis on collaborative learning; approximately 80 percent of our work is done in teacher-assigned groups of three to five students. This forces students who want to complete their assignments into the position of having to discipline peers who won’t behave and coax reluctant group members into contributing.

Distance learning gives me more control of my studies. I can focus more time on subjects that require greater effort and study. I don’t have to sit through a teacher fielding questions that have already been answered. I can still collaborate with other students, but much more effectively. I am really enjoying FaceTiming friends who bring different perspectives and strengths to the work; we challenge one another and it’s a richer learning experience.

I’ve also found that I prefer some of the recorded lessons that my teachers post to Google Classroom over the lessons they taught in person. This year I have struggled with math. The teacher rarely had the patience for questions as he spent at least a third of class time trying to maintain order. Often, when I scheduled time to meet with him before school, there would be a pileup at his door of students who also had questions. He couldn’t help us all in 20 minutes before first period. Other times he just wouldn’t show up.

With distance learning, all of that wasted time is eliminated. I stop, start and even rewind the teacher’s recording when I need to and am able to understand the lesson on the day it’s taught. If I am confused, I attend my teacher’s weekly online office hours (which are 60-90 minutes long); there are never more than two or three other students present.

The fact that I am learning so much better away from the classroom shows that something is wrong with our system. Two weeks ago, my school began experimenting with live video teaching on Google Meet. Unfortunately, the same teachers who struggle to manage students in the classroom also struggle online.

What lessons from remote learning can be taken back to the classroom? I have a few suggestions. First, teachers should send recorded video lessons to all students after class (through email or online platforms like Google Classroom). Second, teachers should offer students consistent, weekly office hours of ample time for 1-to-1 or small group meetings. Third, teachers who are highly skilled in classroom management should be paid more to lead required trainings for teachers, plus reinforcement sessions as needed.

These first two suggestions began during distance learning and have already been a great success. I hope they continue when we return to school, and that schools use this opportunity to improve the learning experiences of all their students .

Veronique Mintz is an eighth-grade student.

What are your questions about the post-coronavirus future?

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

Follow The New York Times Opinion section on Facebook , Twitter (@NYTopinion) and Instagram .

“Every morning I take two steps to my desk…”: students’ perspectives on distance learning during the COVID-19 pandemic

• Open access
• Published: 22 January 2024
• Volume 88 , pages 1483–1502, ( 2024 )

Cite this article

You have full access to this open access article

• Marco Chiodaroli   ORCID: orcid.org/0000-0002-9010-7658 1 ,
• Lisa Freyhult   ORCID: orcid.org/0000-0002-3600-5087 1 ,
• Andreas Solders   ORCID: orcid.org/0000-0003-4725-3083 1 ,
• Diego Tarrío   ORCID: orcid.org/0000-0002-9858-3341 1 &
• Katerina Pia Günter   ORCID: orcid.org/0000-0002-8520-2642 2 , 3 , 4  

1575 Accesses

9 Altmetric

Explore all metrics

During the COVID-19 pandemic, distance learning became the predominant teaching method at most universities, exposing students and teachers alike to novel and unexpected challenges and learning opportunities. Our study is situated in the context of higher physics education at a large Swedish university and adopts a mixed-methods approach to explore how students perceive shifts to distance learning. Quantitative student survey responses comparing distance learning during the pandemic with previous in-person learning are analyzed with k-means cluster analysis and with a random-intercept multilevel linear model. Combined analyses produce a consistent picture of students who report having experienced the greatest challenges. They are on average younger, report being less autonomous in their learning, and find it harder than peers to ask questions to the instructor. They are also less likely to have access to a place where they can study without interruptions. Variation across courses is small with students being largely subjected to the same set of challenges. Qualitative data from semi-structured focus group interviews and open-ended questions supports these findings, provides a deeper understanding of the struggles, and reveals possibilities for future interventions. Students report an overall collapse of structure in their learning that takes place along multiple dimensions. Our findings highlight a fundamental role played by informal peer-to-peer and student-instructor interactions, and by the exchange of what we refer to as “structural information.” We discuss implications for teachers and institutions regarding the possibility of providing support structures, such as study spaces, as well as fostering student autonomy.

Similar content being viewed by others

First-Year University Students in Distance Learning: Motivations and Early Experiences

Distance learning environment: perspective of Italian primary and secondary teachers during COVID-19 pandemic

Distance Learning in the Post-pandemic Period: Trends, Limitations and Symmetry of Knowledge

Avoid common mistakes on your manuscript.

Introduction

“(...) At night I take two steps back and go to bed.”

Distance learning (DL) Footnote 1 moves learning from the physical classroom to a technology-moderated space of communication and information exchange. During the COVID-19 pandemic, instruction was moved online with very short notice, presenting teachers and students alike with novel challenges. A growing body of literature explores this shift to DL. The lack of face-to-face interaction with teachers and peers is emphasized as a particularly critical factor in multiple national and disciplinary contexts, such as higher education in Pakistan (Adnan & Anwar, 2020 ), Germany (Hoss et al., 2021 ), the USA (Serhan, 2020 ), and Turkey (Yazgan, 2022 ), as well as chemistry higher education in the USA (Jeffrey & Bauer, 2020 ) and physics higher education in Italy (Marzoli et al., 2021 ). A lack of interaction negatively affected engagement, interest, and motivation of students and even changed students’ attitudes towards the subject itself (e.g., Marzoli et al, 2021 ). Meta-studies highlight the importance of supporting students’ social needs and guaranteeing the continuity of education (Bozkurt et al., 2022 ). As the pandemic has accelerated the trend of making DL more mainstream, teachers and institutions are now presented with the question of which of the enacted changes in education delivery should be kept in non-pandemic times (Sharaievska et al., 2022 ).

Students display mixed reactions to the shift to DL, with negative attitudes, on average, being more prominent than positive attitudes (Hoss et al., 2021 ; Sharaievska et al., 2022 ; Yazgan, 2022 ). Not all students respond to the transition in the same way, prompting the question on why some groups respond more positively than others (Sharaievska et al., 2022 ). While student attitudes and perceptions in general have been intensely investigated, Bond et al. ( 2021 ) highlight a lack of consideration for marginalized and vulnerable populations. Identifying potentially vulnerable students is therefore crucial to ensure that nobody is left behind. Evidence shows that international students (Whatley & Castiello-Gutiérrez, 2022 ), black students and students from ethnic minorities (Arday & Jones, 2022 ; Donham et al., 2022 ; Lederer et al., 2021 ), and students with disabilities (Bartz, 2020 ) are particularly vulnerable. At the level of individual students, several factors have been reported to affect responses to DL, such as autonomy and self-regulation (Broadbent & Poon, 2015 ; Pintrich, 2004 ; Zimmerman, 2002 ). Motivation, self-efficacy, and use of technology are linked to better engagement and increased academic performance (Aguilera-Hermida, 2020 ). Students who have access to, and higher competence using, computers also display more positive attitudes to DL (Terzi et al., 2021 ). First-year students have been recognized to struggle with self-regulation, self-organization, and communication skills (Klein et al., 2021 ). Other studies find that education level and seniority correlate with student attitudes (Malkawi et al., 2021 ).

With DL being an umbrella term encompassing very different implementations, it is important to attend to which structures, practices, and tools lead to better outcomes for different groups of students. Among the studies that investigate this issue in the context of the pandemic, the difference between synchronous and asynchronous online teaching appears to be particularly relevant (Guo, 2020 ). Synchronous collaborative tools have been found to be common in studies conducted during the first year of the pandemic (Bond et al., 2021 ). Several studies have argued that teaching methods such as flipped classrooms Footnote 2 (Lage et al., 2000 ) alleviated some of the negative aspects of the shift to DL (Divjak et al., 2022 ). From a constructivist perspective on learning, instructors shape learning environments, yet students are responsible for their learning (Bransford et al., 2000 ). Tanner ( 2013 ) highlights the importance of providing structure that invites all students to participate in learning and to shape an inclusive classroom environment, providing another angle from which different implementations of DL can be assessed.

In this study, we combine perspectives on affordances and challenges of the shift to DL with a focus on which student groups are particularly vulnerable and which methods students understand to positively affect their experiences. Grounded in the framework of Community of Inquiry (CoI), we employ a mixed-methods approach to build on and reach beyond findings of previous studies. In the context of higher education in Physics and Engineering at a large Swedish university which transitioned to DL in the spring of 2020, we center students’ experiences and attitudes and ask:

Which specific challenges do students report in the shift to DL?

Which student groups are particularly affected by these challenges?

Which teaching methods and practices are associated with better outcomes?

Theoretical framework

In order to address the complexity of students’ DL experiences from multiple perspectives, we draw on the CoI as a robust theoretical foundation (Castellanos-Reyes, 2020 ; Garrison et al., 2010 ). The CoI framework recognizes three interconnected elements for higher-order learning: cognitive, teaching, and social presence (Garrison & Arbaugh, 2007 ; Garrison et al, 2000 ). Social presence is defined as “the ability of participants in a CoI to project themselves socially and emotionally as real people” (Garrison et al., 2000 ,89). It stands in contrast to the potential deficit of social interaction in a DL context and is characterized in terms of “the participants identifying with the community, communicating purposefully in a trusting environment, and developing interpersonal relationships” (Garrison et al, 2010 ,7). The positive relationship between CoI presences and self-efficacy, motivation, and student relationships has already been pinpointed in the context of the pandemic (Harrison, 2021 ; Donham et al., 2022 ). Scott et al. ( 2016 ) also find that students value and engage in online informal learning spaces, showing high levels of social presence, as well as cognitive, learning, and teaching presences. Additionally, Shea & Bidjerano ( 2010 ) propose the CoI to be augmented to cover learning presence as an additional pillar, explicitly including and highlighting learners’ active role in technology-mediated environments.

While grounded in the CoI framework, we also draw from the broader concept of learning communities, which West & Williams ( 2017 ) define along access, relationships, visions, and functions. Feeling part of a community of learners is positively related to engagement, learning, and educational outcomes (Akyol & Garrison, 2011 ; Arbaugh, 2008 ). Students’ understanding of their learning communities during the pandemic can be understood in terms consistent with West and Williams’ model, also highlighting the impact of isolation and lack of informal interaction in and beyond the classroom on community creation (Prodgers et al., 2022 ). Community building has been recognized as a necessary step in addressing vulnerability (Powietrzynska et al., 2021 ), leading to a call on institutions to play a role in alleviating the social challenges students face (Branchu & Flaureau, 2022 ).

Methodology

We adopt a mixed-methods approach (Johnson & Onwuegbuzie, 2016 ) that combines a quantitative student survey, an open-ended question, a semi-structured focus-group interview, and the collection of course-specific information from instructors (see Fig.  1 ).

Schematic representation of our research methodology with references to tables in the article

Quantitative and qualitative data are triangulated and contribute equally to addressing the first research question. We draw primarily on quantitative data when answering the second and third questions. In addressing the second research question, we employ cluster analysis (Antonenko et al, 2012 ; Everitt et al., 2011 ) as an exploratory analytic tool to divide respondents in homogeneous groups according to their response to the shift to DL. Cluster analysis is a commonly used method to identify hidden patterns, structures, and relationships within a data set, by revealing groups of similar data points (see, e.g., McNally et al., 2017 ). Addressing the last research question necessitates the collection of course-specific information. Given its multilevel structure (by course and individual student), this data was analyzed using a random-intercept multilevel linear model (Snijders & Bosker, 2012 ).

Participants and setting

At the Swedish university’s Department of Physics and Astronomy, all teaching was moved online in March 2020, while some in-person teaching (e.g., laboratory work) was resumed consecutively. As Sweden never experienced a strict lockdown, students had access to campus facilities, albeit with restrictions. Students and teachers were, however, encouraged to work remotely.

Students from all courses offered by the department between the onset of the pandemic and the end of spring 2021 were invited to participate in the survey through emails distributed by course coordinators. The majority of students in our study are enrolled in an Engineering or Physics program, ranging from first-year undergraduate to second-year master level. The survey had 571 individual responses (293 male, 172 female, and three gender non-binary students). 396 students started their undergraduate education at a Swedish university, 71 at non-Swedish universities. Ages ranged from 18 to 69 years (mean = 23.4 years). We excluded responses in which no specific DL course was mentioned or when students referred to courses from outside the department, resulting in 540 participants included. 361 answered the open-ended questions. Of the 540 student answers, 413 could be used for cluster analysis, after removing answers from students who already had answered the survey for another course and incomplete answers. 470 responses were complete enough to be used in the multilevel analysis, for a total of 89 different courses. Among these, specific information could be collected from instructors and cross-referenced for 50 courses. 24 courses were mostly synchronous, 10 mostly asynchronous, and 12 had a mixed format. Among the methods employed, 25 courses involved in-class group work, 16 used flipped classrooms, and nine just-in-time teaching. Less common strategies include think-pair-share and scheduling extra time for students to meet without the teacher.

All students who answered the survey were invited to the focus-group interview. Eight students responded to the call, and we invited six students, ensuring to represent different programs, courses, and genders. Four students (three men, one woman) joined the online semi-structured focus group interview (Longhurst, 2003 ), which was conducted by two of the authors who were not involved in the students’ teaching.

Swedish universities do not have formal requirements on ethical approval for research that does not involve physical interventions on people, human tissues, or the collection and processing of sensitive personal data (as classified by the Swedish law 2003:460 and the General Data Protection Regulation of the European Union, gender and age are considered non-sensitive data). Participants were informed about the purpose of the study, voluntarity of their participation, protection of their identities through anonymization, and processing and storage of the data collected, and provided their informed consent. We use pseudonyms to preserve participants’ anonymity.

Quantitative survey

The quantitative portion of the student survey was divided into three parts. Aside from age, gender, and international-student status, the first part included questions designed to identify the extent to which the respondent reports being able to learn autonomously. Drawing from our theoretical framework, we also included questions aimed to probe the extent to which the respondent relies on social interaction with peers for learning and feels a sense of belonging (cf. Akyol & Garrison, 2011 ; Arbaugh, 2008 ; Branchu & Flaureau, 2022 ; Powietrzynska et al., 2021 ). Furthermore, students were asked several stand-alone questions (see Table  1 ).

The second part of the survey compares students’ perception of DL to their previous experience with in-person teaching without reference to a specific course (see Table  2 ). This comparison is similar to the one in McNally et al. ( 2017 ), where flipped classrooms were compared to regular teaching. These questions are designed to explore various aspects of learning and are analyzed with k-means cluster analysis, Footnote 3 assuming the students could be grouped into two or more clusters based on how they rate their experience with DL, and using the silhouette method to assess the number of clusters that best describes the data. Answers to the first part of the survey are then compared using the Welch’s t test to identify differences between clusters. Footnote 4 We note that cluster analysis has already been employed to divide students in different groups based on their response to the shift to DL (Biwer et al., 2021 ; Toumpalidou & Konstantoulaki, 2023 ).

In the third part of the survey, students were asked to identify and refer to a specific course taught with DL. They were asked to compare 12 different aspects of their learning experience with previous courses they had taken in person. Based on the open-ended answers, eight of the 12 items were selected as corresponding to major factors affecting the shift to DL (see Table  3 ). Among these, items 1–7 were grouped to construct a course-specific outcome variable, while item 8 was treated separately, since it correlated weakly with the others. Information about specific courses was cross-referenced with a teacher survey and also complemented with information taken from the courses’ websites regarding enrollment and presence of specific instruction methods. Given its multilevel structure, this data was analyzed using a random-intercept multilevel linear model. The possibility of cross checking level-one variables in the multilevel model with the results of cluster analysis was designed to provide a robustness test on the identification of individual student factors affecting responses to DL.

Open-ended question and focus group interview

In the survey, students are asked in an open-ended question to name three major factors that affected their learning in comparison with previous semesters and to explain how. Answers were analyzed using thematic analysis (Braun & Clarke, 2006 ), identifying the most common themes and whether they were referred to positively, negatively, or neutrally. The coding was done by two of the authors independently at first and re-coded after agreeing on a common set of themes.

The semi-structured focus-group interview (Longhurst, 2003 ) was structured along: learning; students’ autonomy; role of peer interactions; teachers’ role and interactions Footnote 5 ; and structure and clarity (the interview schedule is in the supplemental material ). We, for instance, asked students what kind of teaching they experienced with DL and how it was different from experiences before the pandemic and posed follow-up questions expanding on given answers and to ensure comprehension. Particular attention was paid to equal distribution of speaking time and the opportunity for all participants to contribute. The interview was conducted in English. After closing the interview, the two interviewers reflected together on what they considered the main themes as inspired by reflexive ethnography (Davies, 2008 ). Interview recordings were transcribed using AI-based transcription software and revised and anonymized by one of the interviewers. All research-group members conducted a thematic analysis on the interview transcript (Braun & Clarke, 2006 ), and themes were iteratively discussed in the research group.

Following the iterative process on the focus group data, we jointly analyzed the open-ended question and the focus group transcript applying preliminary themes and refining them into seven final themes. These themes were furthermore triangulated with the quantitative data, identifying Likert questions in the survey that are relevant for each theme and using descriptive statistics (see the supplemental material ).

This section merges responses from the open-ended survey question and the focus group interview and presents results from the quantitative part of the survey. We triangulate the qualitative analysis with quantitative findings, identifying complex relationships between the various themes. More information about the students can be found in the supplemental material .

The collapse of structure

We describe seven intertwined themes: interaction with other students, interaction with the teacher, environment, autonomy, collecting and sharing information, motivation, and understanding the content. While these themes can stand for themselves, they are all strongly interwoven with one another and, as we will show, tied together by one identifier, a collapse of structure . This sentiment becomes visible in Magnus’ title quote:

Every morning I take two steps from my bed to my desk, and sit down at my computer. At night, I take two steps back and go to bed. Time no longer has meaning. I have not met anyone my age in forever. Everything is gray. (…) I don’t feel like I have study time and I don’t feel like I have free time. I only feel like I have time. (Magnus)

In the following, we will map out the different themes, how themes are intertwined with each other, and how they contribute to the collapse of structure.

Interaction with other students

A prominent theme is the social presence of peers. This includes formal and spontaneous conversations and interactions. Students describe that interactions with other students became more difficult, which made it harder to have group discussions and ask for and receive help.

Cecilia misses to “discuss with the others in the same course” and states that everyone is “so, yeah, all over, all over the country”. Johan feels isolated, too. He shares, “It was hard to chat with other students. I got the feeling that I was the only one thinking the material was difficult, and thus I dared not ask questions.” At the same time, other students mentioned positive effects of investing less energy into social interactions. Esben shows the multidimensionality of social presence and describes strategies to compensate for the lack of interactions with other students in DL,

I mean, the social part is really important. And we had a lot of isolation in the beginning, I would say, in my class. So we actively started studying together (…) and just having each other on (…) speaker, not even doing the same assignments, just having someone there. You can hear the person breathing. You know, if there is a question, you just say it out there to the room, basically, but you get the answer at least. (Esben)

Interaction with teachers

Similarly to interactions with other students, having interactions and conversations with instructors is perceived as more difficult. This includes the formal dimension of, e.g., asking questions during class, but also having informal conversations.

Our findings indicate that students used to rely on diverse structures created through the presence of teachers. Aside from formal discussions of content and guidance from the teacher(s), informal conversations about course material also supported students in their learning. Kajsa states that, in DL, there were no spontaneous interactions with instructors, “It is almost impossible to make small-talk during for example breaks with instructors about the subjects discussed in class, especially when you do not have an explicit question about a certain concept but might need some help organizing your thoughts.”

Tom highlights that he feels disconnected from both peers and the instructors and that interactions in a DL setting might be more important than with in-person teaching. He shares, “I think that’s really important to bear in mind in such a transition that teachers need to be as present, if not even more present.” He connects a lack of presence and interactions to a lack of guidance:

And I mean, I have almost, if not daily, at least weekly contact with my teacher. But I think far from everyone has that. I've sort of stopped having any social contact with anyone in that class. And so I think that's not a very helpful resource from a teacher just giving out notes. Then you could just read a book by yourself. I mean, the point of having a teacher is to be sort of a guidance to guide you as a group through something. (Tom)

Students remark on the lack of informal interactions in which they can ask questions. In the words of one student (Karl): “generally I would like to have a talk with [the] lecturer and clarify some bigger or more fundamental questions after lecture, but with DL, it’s more difficult to do so.”

Environment

The impact of the environment on the students is twofold. Firstly, a lack of change in the environment made it harder for students to focus, as learning spaces and life merged into one. Secondly, students reported that they did not have access to study spaces that allowed for uninterrupted and focused study time.

Similarly to Magnus, who provided the quote used in our title, Maja describes that, “It is hard to stay focused when you are not in an environment where you are supposed to learn. I study, eat, entertain myself and relax at the same desk in my 19 sqm room.” While that shows the difficulty of separating learning and studying spaces, Gustav shares that it was hard to find spaces in which he “didn’t get interrupted”. Esben not only describes those challenges, but also that they lead to being frustrated “due to a lot of distractions around in your own apartment.”

A significant part (14.9%) of the survey respondents report to not have access to good study spaces, while 15.5% takes a neutral stance. Access to good study spaces is one of the strongest quantitative predictors of the difficulty experienced in the shift to DL. A lack of physical structures, learning spaces, and life spaces merging negatively impacted students’ focus, motivation, and perceived learning success.

Another theme was grappling with autonomy, especially in relation to time management and study structure. While increased autonomy in combination with access to low-distraction study spaces provided some students with more time to study, many struggled with increased autonomy and a lack of structure when managing their time and information.

While Lovisa, as a 6th year student, has developed skills to work independently, she also highlights the ambiguity of increased autonomy in relation to spaces. She shares,

Not having to leave the house to study and attend classes was both good and bad. The lines between free time and studies were very blurred, which has made it a little difficult to take time to rest, but I somehow also feel a lot less stressed out. Taking exams at home, in a safe and comfortable space also helped with stress. (Lovisa)

Peter describes how perception on time is different because of a lack of structure and time management. He says,

I mean, like I don’t feel like I have time, that it’s like study time and I don’t feel I have free time. I only have, like, time and since all the lectures are recorded, if I don’t feel like taking a lecture now, I take it later. And that’s like, it gets more stressful, you know. To always have that availability to stop it and demand it. (Peter)

Collecting and sharing information

A fifth theme caught our attention. Students reported that more course information and material was available to them, and though helpful, it required structure both from the instructors’ and from the students’ side to make efficient use of the information. In analyzing student responses, we find it useful to differentiate between course content, and what we call “structural information,” namely information about, e.g., deadlines and material, which in a DL setting is less readily available. Erik shares,

While studying on campus, I did not plan very much and simply went with the flow. Most [teachers] reminded us of important hand-ins and when documents have been updated on Studentportalen. Footnote 6 When not, there were always some people in class who kept up with work, sometimes me, sometimes others. Together we made sure everyone was on track with what was due. This disappeared entirely while studying [remotely]. (Erik)

It is here this theme strongly intersects with the social aspect of learning and teaching in person. Esben makes this entanglement more explicit and reflects, “you meet people and you talk and you plan your work. And if you have missed something, then you’ll hear about it (…). So everyone is each other’s social net and also like study net.” He continues,

But now you have to keep track of everything yourself. So I find that I start my day by going into Studentportalen and see what’s new and if something has been updated, there have been cases where a lot of documents have been updated where we haven't done the information about it. You have to check that your documents are up to date. You know, there’s so much energy going into planning and (…) how to study. And so less time is actually going into studying. (Esben)

In the words of Selma, it was

harder to know if you are keeping up okay with the course, since you don’t daily interact with other students and talk about the courses. Some parts were barely mentioned but seemed essential on the exams. This was partly due to lack of information and important information embedded in otherwise useless information. (Selma)

The inability to differentiate between important and accessory information can be interpreted as another manifestation of the overarching loss of structure.

While often shared implicitly, struggling with decreased motivation was mentioned explicitly by multiple students. Rebecka feels understimulated and shares that it is hard to keep both one’s mood and one’s motivation up when not being able to interact with peers in the same room. She continues, “You feel very lonely sometimes and bored because not much happens.” Sven mentions, “My motivation was lacking because of all distractions, otherwise pretty much the same.” While distractions and loneliness were mentioned as causing a lack of motivation, Anders describes that the lack of motivation is also caused by a general feeling of disconnectedness from learning spaces and practices, “In a rational sense it’s obvious that I’m studying, but unconsciously it feels like I am on study leave. Imagine that you go to the Bahamas for a sun bathing trip and then a colleague sends you some emails of things they want you to do.”

Understanding content

In line with the sentiment of a lack of structure, with spaces and practices merging into one, causing disconnectedness and a lack of motivation, students also highlighted how that sentiment leads to difficulties understanding the content. Edvin shares, “Now I frequently forget things even though I studied it alot; it sort of just becomes part of everything else that is exactly the same as everything else.” Similarly, Klas reflects on how disconnectedness and loneliness made it difficult to understand content. He writes, “Also the loneliness from not meeting other students and study with them made it harder to understand everything.”

The interconnection between cognitive and social presence is illustrated by Louise, who highlights,

Not having the same opportunity to talk to my classmates about course work or other matters made me think of my work more narrowly. I spent more time memorizing rather than thinking of the coursework more deeply. (Louise)

Cluster analysis and multilevel models

A silhouette analysis suggests that two clusters best represent responses to questions in Table  2 (see the supplemental material ). These contain 238 and 175 data points and are dubbed “more affected” and “less affected” students, respectively. Students in the two clusters differ with respect to some of the factors probed in the first part of the survey, as displayed in Table  4 . Cronbach’s alphas for the social and autonomy scales were 0.73 and 0.77, respectively.

Random-intercepts models were used to fit an outcome variable Footnote 7 constructed from responses to questions in Table  3 using the residual maximum likelihood method. These models are presented in Table  5 . Model 1 only includes student-level explanatory variables. Model 2 also includes course-level variables.

Students experiencing the largest challenges

Students in the more affected cluster, as well as students with a lower value of the outcome variable for the multilevel models, score lower on the autonomy scale, are younger in age, find it generally more difficult to ask questions to the instructor, and are less likely to report that they feel more comfortable taking part in the courses without being seen and that they have access to a place where they can study without interruptions. We do not observe any significant difference regarding previous experience with DL, time from the beginning of the pandemic, gender, or international-student status. Notably, we observe a negative correlation between the outcome variable and both average reported grade and social-learner scale in the multilevel models, but not in the cluster analysis.

Variation across courses and choice of methods

Intraclass correlation coefficients give an estimate of the ratio between inter- and intracourse variance of the outcome variable and take remarkably small values (0.035 and 0.047 for the two models). Model 2 allows us to probe course-specific factors and methods and address our research question 3. Aside from the aforementioned factors at level one, we observe positive effects from reported use of the flipped-classrooms method and from the instructors reporting having access to the equipment needed. Conversely, size of the course, asynchronous or synchronous lecturing, time elapsed since the beginning of the pandemic, and pedagogical methods used (with the exception of flipped classrooms) do not bear any statistically significant effect on the outcome variable. Footnote 8

Discussion and implications

This study explores students’ reactions to the shift to DL in courses offered by the Physics and Astronomy department at a major Swedish university in the first year of the COVID-19 pandemic. While individual courses presented a significant variation regarding, e.g., number of students, level, synchronous and asynchronous moments, and pedagogical methods employed, students report a consistent and common set of challenges, and we find that what instructors did at the level of individual courses had a comparatively small impact. The themes we identified in the thematic analysis are all connected by the common thread of the collapse of structure , especially of structure that we propose to have been taken for granted in pre-pandemic times. This collapse has multiple dimensions. The sudden reduction of peer and instructor interactions can be seen as an erosion of the social-presence pillar in a CoI (Garrison et al., 2010 ), affecting students emotionally and intellectually, leading to feeling disconnected. Students are no longer able to use their interactions with peers to shape their learning, contributing to the collapse of  the other CoI pillars. The physical environment, with a blurred separation between study and private spaces, as well as study and leisure times, provides another dimension. A third dimension is related to the loss of ability to differentiate between important and non-essential course material and information.

Given these results, we should ask: How is structure created? A first answer could focus on externally provided structure from the instructor, traditionally associated with the teaching-presence CoI pillar. In the focus group, students argued that more structure, and even more mandatory hand-in exercises, would help them. They also asked for more informal teaching moments in which they can meet the teacher and ask questions. In the quantitative data, we see that a reported use of flipped classrooms is correlated with better outcomes, which can be explained by the attempt of regulating and providing structured activities outside the classroom. Simultaneously, students themselves contribute to shaping and structuring their learning, in ways that appear to have been taken for granted before the shift to distance learning. To disentangle these mechanisms of structure creation, we differentiate between course content and meta-level information dubbed “structural information”. While this is seen through the lenses of the CoI framework as a manifestation of the social presence pillar, the role of information exchanges here assumes a dimension that was not previously recognized: students draw an explicit connection between lack of informal face-to-face interaction and increased difficulties accessing structural information. This is also in line with observations in the literature that students found it harder to understand expectations and standards of work due to lack of a social network during the period of DL (e.g., Elmer et al., 2020 ; Neuwirth et al., 2020 ; Warfvinge et al., 2022 ). Non-content language used by instructors in classrooms has recently been recognized as a key for better understanding classroom dynamics (e.g., Harrison et al., 2019 ; Seidel et al., 2015 ). Here, we argue that such language plays an equally important role in student–student interaction. Structural information also plays a role in functional cohesion of a learning community as defined by West & Williams ( 2017 ).

We can see that some students coped better with the DL conditions. In fact, the comparatively small variation in outcome observed across courses has its counterpart in a large variation at the level of individuals. Tanner ( 2013 ) highlights the diversity of students’ needs and the importance for structuring the classroom in a way that fosters engagement and inclusivity. This observation can be used to draw a direct line between the observed collapse of structure and the vulnerability of some groups of students. We find that the students who are most vulnerable in the transition to DL are the ones who are most dependent on the externally provided structure. They, on average, are younger, score lower on our measure of student autonomy, report finding it more difficult to ask questions to the instructor, and are less likely to have access to a suitable place to study. Age may correlate with increased experience studying in a higher education setting, while our measure of student autonomy, as well as ease interacting with the instructor, may also have a mitigating effect on the collapse of structure we have observed.

Limitations

There are factors that may affect response to DL we did not investigate in our study. Most prominently, the students’ ethnic background, as well as (dis)ability, and socioeconomic status were not included in the questionnaire. The literature has already identified students from underrepresented groups (Arday & Jones, 2022 ; Donham et al., 2022 ; Lederer et al., 2021 ) and students with disabilities (Bartz, 2020 ) as particularly vulnerable groups. Our finding that availability of appropriate study spaces strongly correlates with better outcomes indirectly highlights how students with lower socioeconomic status might have experienced particular challenges. We regard not including a more detailed and intersectional perspective as one of the main limitations of our study. In addition, recruitment of study participants might implicitly overrepresent the perspectives of students more adapted to a DL setting since the survey and focus group were both conducted online. Several questions were based on students’ self-reporting and recollection, for example concerning their understanding of the material. The identification of courses using flipped classrooms was based on teachers' self-reporting.

Implications for teachers and institutions

Our results suggest that teachers developing DL implementations should address the increased need for structure, as well as facilitate information exchanges among students. While flipped classrooms appear to be correlated with better outcomes, we interpret this as a result of a method aiming to regulate and structure students’ activities outside of the classroom. Some students also report favorably on a course offering spaces in which students can interact informally. However, the small value of the intraclass correlation highlights that what happened outside of the virtual classrooms has been as important as what happened inside, if not more. Aside from factors linked to individual students, we need to recognize the fundamental role played by institutions in responding to the shift to DL. The identification of vulnerable students can play a role in informing and directing the action of universities faced with similar challenges in the future, as well as more conventional implementations of DL. For example, universities may wish to prioritize options for in-person learning, if these are available, for academically younger students. Student autonomy emerges as one of the fundamental factors that determine the adjustment to DL. Universities should take measures to support students in developing robust study techniques, for example by offering courses on learning methods. The study has emphasized that the lack of a proper physical study environment affects many students, in agreement with similar results (Neuwirth et al., 2020 ). It is therefore essential that support structures such as study spaces are offered. We suggest that institutions invest resources in, and pay particular attention to, providing students with sufficient virtual and physical spaces for informal interaction to further enhance their sense of community (Branchu & Flaureau, 2022 ). This is in line with the reconceptualization of the notion of vulnerability proposed by work that argues to adopt a relational view of vulnerability rather than insisting on individual features and dispositions (e.g., Jackson, 2018 ). Instead of advocating for giving more resources to individuals, Jackson ( 2018 ) proposes to address the problem on a structural level, e.g., by building trust and communication with students. It is also essential to promote acceptance of vulnerabilities and cultivation of resilience (Powietrzynska et al., 2021 ).

Implications for theory

Our study can be understood as a setting in which the CoI social-presence pillar is drastically reduced. The fact that our measure of student autonomy is associated with better outcomes favors variations of the CoI that incorporate a fourth pillar for learning presence, such as the one suggested by Shea & Bidjerano ( 2010 ). Our findings can be understood in the context of the CoI framework, where the overarching collapse of structure has its counterpart in a deterioration of all three pillars. Mechanisms for structure creation and, in particular, the pivotal role played by structural-information exchanges among students did not receive adequate attention in the past and have the potential to add further nuance both to the social presence and teaching presence categories. Some authors have already observed that student–student communication is characterized by a high level of teaching presence (Scott et al., 2016 ), while our results indicate a deterioration of the teacher’s own social presence. Interestingly, this blurs the lines between CoI pillars, enlarging the importance of the learning presence . Ultimately, further investigation is warranted on the mechanisms underlying structural information exchanges.

Data Availability

Anonymized data can be made available on reasonable request.

We use distance learning as a term encompassing implementations ranging from Emergency Remote Teaching in the first months of the pandemic (Bozkurt et al., 2020 ; Hodges et al., 2020 ) to instructional designs for which more preparation time has been available.

With flipped classrooms, students asynchronously view and prepare material outside of class, while active and collaborative learning is prioritized in synchronous moments.

k-means clustering is a commonly used algorithm in unsupervised machine learning to partition a given data set into k groups. Each cluster is represented by its center, determined by the mean of the points assigned to the cluster.

It is debated whether parametric methods like k-means clustering and the Welch’s t test could be applied to Likert data, which is generally considered to be ordinal. A common approach is to consider individual Likert items with few points as ordinal and items with many points, or scales consisting of several items, as interval data (Wu & Leung, 2017 ). To account for this, a non-parametric approach, using K-modes clustering and the Mann-Whitney U test, is reported in the supplemental material . These results agree with the parametric analysis.

In our study, “teacher” and “instructor” are used to indicate university teachers at all levels, including professors, lecturers, and researchers with instructional responsibilities.

The learning management system.

The outcome variable is constructed combining questions 1–7 in Table  3 . While answers to these questions correlate strongly, with correlation coefficients ranging between 0.29 and 0.63 and Cronbach’s alpha = 0.84, time spent looking for information correlates weakly only with some of the other questions, indicating that all students have on average spent more time regardless of other factors. For this reason, question 8 is not included in the outcome variable.

A Jarque Bera test was conducted to confirm that the residuals are normally distributed. Homogeneity of variance was also confirmed with Levene’s test. See the supplemental material for additional checks.

Adnan, M., & Anwar, K. (2020). Online learning amid the COVID-19 pandemic: students’ perspectives. Journal of Pedagogical Sociology and Psychology, 2 (1), 45–51. https://doi.org/10.33902/JPSP.2020261309

Article   Google Scholar  

Aguilera-Hermida, A. P. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open, 1 , 1–8. https://doi.org/10.1016/j.ijedro.2020.100011

Akyol, Z., & Garrison, D. R. (2011). Understanding cognitive presence in an online and blended community of inquiry: Assessing outcomes and processes for deep approaches to learning. British Journal of Educational Technology, 42 (2), 233–250. https://doi.org/10.1111/j.1467-8535.2009.01029.x

Antonenko, P. D., Toy, S., & Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60 , 383–398. https://doi.org/10.1007/s11423-012-9235-8

Arbaugh, J. B. (2008). Does the Community of Inquiry Framework Predict Outcomes in Online MBA Courses?. The International Review of Research in Open and Distributed Learning , 9(2). https://doi.org/10.19173/irrodl.v9i2.490

Arday, J., & Jones, C. (2022). Same storm, different boats: The impact of COVID-19 on Black students and academic staff in UK and US higher education. Higher Education, 25 , 1–22. https://doi.org/10.1007/s10734-022-00939-0

Bartz, J. (2020). All Inclusive?! Empirical insights into individual experiences of students with disabilities and mental disorders at German universities and implications for inclusive higher education. Education Sciences, 10 (9), 223. https://doi.org/10.3390/educsci10090223

Biwer F, Wiradhany W, oude Egbrink M, Hospers H, Wasenitz S, Jansen W and de Bruin A (2021) Changes and Adaptations: How University Students Self-Regulate Their Online Learning During the COVID-19 Pandemic. Frontiers in Psychology 12, 642593. https://doi.org/10.3389/fpsyg.2021.642593

Bond, M., Bedenlier, S., Marín, V.I., & Händel, M., (2021). Emergency remote teaching in higher education: mapping the first global online semester. International Journal of Educational Technology in Higher Education 18, 50. https://doi.org/10.1186/s41239-021-00282-x

Bozkurt, et al. (2020). A global outlook to the interruption of education due to COVID-19 pandemic: Navigating in a time of uncertainty and crisis. Asian Journal of Distance Education, 15 (1), 1–126. https://doi.org/10.5281/zenodo.3878572

Bozkurt A, Karakaya K, Turk M, Karakaya Ö, Castellanos-Reyes D. (2022) The impact of COVID-19 on education : a meta-narrative review. TechTrends, 66 (5), 883–896. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9255479/

Branchu, C., & Flaureau, E. (2022). “I’m not listening to my teacher, I’m listening to my computer”: Online learning, disengagement, and the impact of COVID-19 on French university students. Higher Education . https://doi.org/10.1007/s10734-022-00854-4

Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn (Vol. 11). National academy press.

Google Scholar  

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 (2), 77–101.

Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27 , 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007

Castellanos-Reyes, D. (2020). 20 years of the community of inquiry framework. TechTrends, 64 , 557–560. https://doi.org/10.1007/s11528-020-00491-7

Davies, C. A. (2008). Reflexive ethnography: A guide to researching selves and others . Routledge.

Divjak, B., Rienties, B., Iniesto, F., Vondra, P., & Žižak, M. (2022). Flipped classrooms in higher education during the COVID-19 pandemic: findings and future research recommendations. International Journal of Educational Technology in Higher Education 19, 9. https://doi.org/10.1186/s41239-021-00316-4

Donham, C., Barron, H. A., Alkhouri, J. S., Kumarath, M. C., Alejandro, W., Menke, E., & Kranzfelder, P . (2022). I will teach you here or there, I will try to teach you anywhere: perceived supports and barriers for emergency remote teaching during the COVID-19 pandemic. International Journal of STEM Education 9 (19). https://doi.org/10.1186/s40594-022-00335-1

Elmer, T., Mepham, K., & Stadtfeld, C. (2020). Students under lockdown: Comparisons of students’ social networks and mental health before and during the COVID-19 crisis in Switzerland. PLoS ONE, 15 (7), e0236337. https://doi.org/10.1371/journal.pone.0236337

Everitt, B., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis. Wiley . https://doi.org/10.1002/9780470977811.ch7

Garrison, D. R., & Arbaugh, J. B. (2007). Researching the community of inquiry framework: Review, issues, and future directions. The Internet and Higher Education, 10 (3), 157–172. https://doi.org/10.1016/j.iheduc.2007.04.001

Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education model. The Internet and Higher Education, 2 (2–3), 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6

Garrison, D. R., Anderson, T., & Archer, W. (2010). The first decade of the community of inquiry framework: A retrospective. The Internet and Higher Education, 13 (1–2), 5–9. https://doi.org/10.1016/j.iheduc.2009.10.003

Guo, S. (2020). Synchronous versus asynchronous online teaching of physics during the COVID-19 pandemic. Physics Education, 55 (6), 065007. https://doi.org/10.1088/1361-6552/aba1c5

Harrison, C. D., et al. (2019). Investigating instructor talk in novel contexts: widespread use, unexpected categories, and an emergent sampling strategy. CBE—Life Sciences Education, 18 , 3.

Harrison, C. D. (2021) Student perceptions of community of inquiry in blended developmental courses during the COVID-19 pandemic . Walden Dissertations and Doctoral Studies, 9743. https://scholarworks.waldenu.edu/dissertations/9743

Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. EDUCAUSE Review , March 27 2020 .

Hoss, T., Ancina, A., & Kaspar, K. (2021). Forced remote learning during the COVID-19 pandemic in Germany: A mixed-methods study on students’ positive and negative expectations. Frontiers in Psychology, 12 , 642616. https://doi.org/10.3389/fpsyg.2021.642616

Jackson, L. (2018). Reconsidering vulnerability in higher education. Tertiary Education and Management, 24 (3), 232–241. https://doi.org/10.1080/13583883.2018.1439999

Jeffrey, K. A., & Bauer, C. F. (2020). Students’ responses to emergency remote online teaching reveal critical factors for all teaching. Journal of Chemical Education, 97 (9), 2472–2485. https://doi.org/10.1021/acs.jchemed.0c00736

Johnson, R. B., & Onwuegbuzie, A. J. (2016). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33 (7), 14–26. https://doi.org/10.3102/0013189X033007014

Klein, P., Ivanjek, L., Dahlkemper, M. N., Jeličić, K., Geyer, M. A., Küchemann, S., & Susac, A. (2021). Studying physics during the COVID-19 pandemic: Student assessments of learning achievement, perceived effectiveness of online recitations, and online laboratories. Physical Review Physics Education Research, 17 (1), 010117. https://doi.org/10.1103/PhysRevPhysEducRes.17.010117

Lage, M. J., Platt, G. J., & Treglia, M. (2000). Inverting the classroom: A gateway to creating an inclusive learning environment. The Journal of Economic Education, 31 (1), 30–43.

Lederer, A. M., Hoban, M. T., & Lipson, S. K. (2021). More than inconvenienced: the unique needs of U.S. college students during the COVID-19 pandemic. Health Education & Behavior, 48 (1), 14–19. https://doi.org/10.1177/1090198120969372

Longhurst, R. (2003). Semi-structured interviews and focus groups. In N. Clifford, M. Cope, T. Gillespie, & S. French (Eds.), Key methods in geography (3rd ed., pp. 143–156). Sage Publications.

Malkawi, E., Bawaneh, A. K., & Bawa’aneh, M. S. (2021). Campus off, education on: UAEU students’ satisfaction and attitudes towards e-learning and virtual classes during COVID-19 pandemic. Contemporary Educational Technology, 13 (1), ep283. https://doi.org/10.30935/cedtech/8708

Marzoli, I., Colantonio, A., Fazio, C., Giliberti, M., di Uccio, U. S., & Testa, I. (2021). Effects of emergency remote instruction during the COVID-19 pandemic on university physics students in Italy. Physical Review Physics Education Research, 17 (2), 020130. https://doi.org/10.1103/PhysRevPhysEducRes.17.020130

McNally, B., Chipperfield, J., Dorsett, P., et al. (2017). Flipped classroom experiences: Student preferences and flip strategy in a higher education context. Higher Education, 73 , 281–298. https://doi.org/10.1007/s10734-016-0014-z

Neuwirth, L. S., Jovic, S., & Mukherji, B. R. (2020). Reimagining higher education during and post-COVID-19: Challenges and opportunities. Journal of Adult and Continuing Education, 27 (2), 141–156. https://doi.org/10.1177/1477971420947738

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16 , 385–407. https://doi.org/10.1007/s10648-004-0006-x

Powietrzynska, M., Noble, L., O’Loughlin-Boncamper, S., & Azeez, A. (2021). Holding space for uncertainty and vulnerability: Reclaiming humanity in teacher education through contemplative equity pedagogy. Cultural Studies of Science Education., 16 , 951–964. https://doi.org/10.1007/s11422-021-10035-x

Prodgers, L., Travis, E., & Pownall, M. (2022). “It’s hard to feel a part of something when you’ve never met people”: Defining “learning community” in an online era. Higher Education . https://doi.org/10.1007/s10734-022-00886-w

Scott, K. S., Sorokti, K. H., & Merrell, J. D. (2016). Learning “beyond the classroom” within an enterprise social network system. The Internet and Higher Education, 29 , 75–90. https://doi.org/10.1016/j.iheduc.2015.12.005

Seidel, S. B., Reggi, A. L., Schinske, J. N., Burrus, L. W., & Tanner, K. D. (2015). Beyond the biology: a systematic investigation of noncontent instructor talk in an introductory biology course. CBE—Life Sciences Education, 14 (4), ar43. https://doi.org/10.1187/cbe.15-03-0049

Serhan, D. (2020). Transitioning from face-to-face to remote learning: students’ attitudes and perceptions of using Zoom during COVID-19 pandemic. International Journal of Technology in Education and Science, 4 (4), 335–342. https://doi.org/10.46328/ijtes.v4i4.148

Sharaievska, I., McAnirlin, O., Browning, M. H. E. M., Larson, L. R., Mullenbach, L., Rigolon, A., D´Antonio, A., Cloutier, S., Thomsen, J., Metcalf, E. C., & Reigner, N. (2022). “Messy transitions”: students’ perspectives on the impacts of the COVID-19 pandemic on higher education. Higher Education . https://doi.org/10.1007/s10734-022-00843-7

Shea, P., & Bidjerano, T. (2010). Learning presence: Towards a theory of self-efficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments. Computers & Education, 55 (4), 1721–1731. https://doi.org/10.1016/j.compedu.2010.07.017

Snijders, T. & Bosker, R. (2012). Multilevel Analysis : an introduction to basic and advanced multilevel modeling, second edition. Sage Publishers.

Tanner, K. (2013). Structure matters: twenty-one teaching strategies to promote student engagement and cultivate classroom equity. CBE—Life Sciences Education, 12 (3), 322–31. https://doi.org/10.1187/cbe.13-06-0115

Terzi, B., Azizoğlu, F., & Özhan, F. (2021). Factors affecting attitudes of nursing students towards distance education during the COVID-19 pandemic: A web-based cross-sectional survey. Perspectives in Psychiatric Care, 57 (4), 1765–1773. https://doi.org/10.1111/ppc.12747

Toumpalidou, S. A., & Konstantoulaki, K. (2023). Education in the pandemic economy: Attitudes towards distance learning as a drive of university students’ decision making. International Journal of Organizational Analysis, 31 (1), 50–62. https://doi.org/10.1108/IJOA-09-2021-2965

Warfvinge, P., Löfgreen, J., Andersson, K., Roxå, T., & Åkerman, C. (2022). The rapid transition from campus to online teaching – how are students’ perception of learning experiences affected? European Journal of Engineering Education, 47 (2), 211–229. https://doi.org/10.1080/03043797.2021.1942794

West, R. E., & Williams, G. S. (2017). “I don’t think that word means what you think it means”: a proposed framework for defining learning communities. Educational Technology Research and Development, 65 , 1569–1582. https://doi.org/10.1007/s11423-017-9535-0

Whatley, M., & Castiello-Gutiérrez, S. (2022). Balancing finances, politics, and public health: International student enrollment and reopening plans at US higher education institutions amid the COVID-19 pandemic. Higher Education, 84 , 299–320. https://doi.org/10.1007/s10734-021-00768-7

Wu, H., & Leung, S. O. (2017). Can Likert scales be treated as interval scales?—a simulation study. Journal of Social Service Research, 43 (4), 527–532. https://doi.org/10.1080/01488376.2017.1329775

Yazgan, Ç. Ü. (2022). Attitudes and interaction practices towards distance education during the pandemic. Education and Information Technologies, 27 , 5349–5364. https://doi.org/10.1007/s10639-021-10843-2

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41 (2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

Download references

Acknowledgements

We are thankful to the Centre for Discipline-Based Education Research in Mathematics, Engineering, Science and Technology (MINT) at Uppsala University for supporting this research and fostering a stimulating environment for education research. We thank Anna Eckerdal, Felix Ho, Cassandra Engeman, Rie Malm, and Bor Gregorcic for insightful comments on our draft. We are also grateful to the three anonymous referees for providing very valuable feedback.

Open access funding provided by Uppsala University.

Author information

Authors and affiliations.

Department of Physics and Astronomy, Uppsala University, Box 516, 75120, Uppsala, Sweden

Marco Chiodaroli, Lisa Freyhult, Andreas Solders & Diego Tarrío

Centre for Gender Research, Uppsala University, Box 527, 75120, Uppsala, Sweden

Katerina Pia Günter

Department of Biology, SEPAL, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA, 94132, USA

Department of Science and Mathematics Education, Umeå University, Naturvetarhuset A, 901 87, Umeå, Sweden

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Marco Chiodaroli .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 527 KB)

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Chiodaroli, M., Freyhult, L., Solders, A. et al. “Every morning I take two steps to my desk…”: students’ perspectives on distance learning during the COVID-19 pandemic. High Educ 88 , 1483–1502 (2024). https://doi.org/10.1007/s10734-023-01179-6

Download citation

Accepted : 29 December 2023

Published : 22 January 2024

Issue Date : October 2024

DOI : https://doi.org/10.1007/s10734-023-01179-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Higher education
• Distance learning
• Mixed methods
• Vulnerability
• Find a journal
• Publish with us
• Track your research