essay about the impact of covid 19 on education

Mission: Recovering Education in 2021

THE CONTEXT

The COVID-19 pandemic has caused abrupt and profound changes around the world.  This is the worst shock to education systems in decades, with the longest school closures combined with looming recession.  It will set back progress made on global development goals, particularly those focused on education. The economic crises within countries and globally will likely lead to fiscal austerity, increases in poverty, and fewer resources available for investments in public services from both domestic expenditure and development aid. All of this will lead to a crisis in human development that continues long after disease transmission has ended.

Disruptions to education systems over the past year have already driven substantial losses and inequalities in learning. All the efforts to provide remote instruction are laudable, but this has been a very poor substitute for in-person learning.  Even more concerning, many children, particularly girls, may not return to school even when schools reopen. School closures and the resulting disruptions to school participation and learning are projected to amount to losses valued at $10 trillion in terms of affected children’s future earnings.  Schools also play a critical role around the world in ensuring the delivery of essential health services and nutritious meals, protection, and psycho-social support. Thus, school closures have also imperilled children’s overall wellbeing and development, not just their learning.   

It’s not enough for schools to simply reopen their doors after COVID-19. Students will need tailored and sustained support to help them readjust and catch-up after the pandemic. We must help schools prepare to provide that support and meet the enormous challenges of the months ahead. The time to act is now; the future of an entire generation is at stake.

THE MISSION

Mission objective:  To enable all children to return to school and to a supportive learning environment, which also addresses their health and psychosocial well-being and other needs.

Timeframe : By end 2021.

Scope : All countries should reopen schools for complete or partial in-person instruction and keep them open. The Partners - UNESCO , UNICEF , and the World Bank - will join forces to support countries to take all actions possible to plan, prioritize, and ensure that all learners are back in school; that schools take all measures to reopen safely; that students receive effective remedial learning and comprehensive services to help recover learning losses and improve overall welfare; and their teachers are prepared and supported to meet their learning needs. 

Three priorities:

1.    All children and youth are back in school and receive the tailored services needed to meet their learning, health, psychosocial wellbeing, and other needs. 

Challenges : School closures have put children’s learning, nutrition, mental health, and overall development at risk. Closed schools also make screening and delivery for child protection services more difficult. Some students, particularly girls, are at risk of never returning to school. 

Areas of action : The Partners will support the design and implementation of school reopening strategies that include comprehensive services to support children’s education, health, psycho-social wellbeing, and other needs. 

Targets and indicators

2.    All children receive support to catch up on lost learning.

Challenges : Most children have lost substantial instructional time and may not be ready for curricula that were age- and grade- appropriate prior to the pandemic. They will require remedial instruction to get back on track. The pandemic also revealed a stark digital divide that schools can play a role in addressing by ensuring children have digital skills and access.

Areas of action : The Partners will (i) support the design and implementation of large-scale remedial learning at different levels of education, (ii) launch an open-access, adaptable learning assessment tool that measures learning losses and identifies learners’ needs, and (iii) support the design and implementation of digital transformation plans that include components on both infrastructure and ways to use digital technology to accelerate the development of foundational literacy and numeracy skills. Incorporating digital technologies to teach foundational skills could complement teachers’ efforts in the classroom and better prepare children for future digital instruction.   

While incorporating remedial education, social-emotional learning, and digital technology into curricula by the end of 2021 will be a challenge for most countries, the Partners agree that these are aspirational targets that they should be supporting countries to achieve this year and beyond as education systems start to recover from the current crisis.

3.   All teachers are prepared and supported to address learning losses among their students and to incorporate  digital technology into their teaching.

Challenges : Teachers are in an unprecedented situation in which they must make up for substantial loss of instructional time from the previous school year and teach the current year’s curriculum. They must also protect their own health in school. Teachers will need training, coaching, and other means of support to get this done. They will also need to be prioritized for the COVID-19 vaccination, after frontline personnel and high-risk populations.  School closures also demonstrated that in addition to digital skills, teachers may also need support to adapt their pedagogy to deliver instruction remotely. 

Areas of action : The Partners will advocate for teachers to be prioritized in COVID-19 vaccination campaigns, after frontline personnel and high-risk populations, and provide capacity-development on pedagogies for remedial learning and digital and blended teaching approaches. 

Country level actions and global support

UNESCO, UNICEF, and World Bank are joining forces to support countries to achieve the Mission, leveraging their expertise and actions on the ground to support national efforts and domestic funding.

Country Level Action

1.  Mobilize team to support countries in achieving the three priorities

The Partners will collaborate and act at the country level to support governments in accelerating actions to advance the three priorities.

2.  Advocacy to mobilize domestic resources for the three priorities

The Partners will engage with governments and decision-makers to prioritize education financing and mobilize additional domestic resources.

Global level action

1.  Leverage data to inform decision-making

The Partners will join forces to   conduct surveys; collect data; and set-up a global, regional, and national real-time data-warehouse.  The Partners will collect timely data and analytics that provide access to information on school re-openings, learning losses, drop-outs, and transition from school to work, and will make data available to support decision-making and peer-learning.

2.  Promote knowledge sharing and peer-learning in strengthening education recovery

The Partners will join forces in sharing the breadth of international experience and scaling innovations through structured policy dialogue, knowledge sharing, and peer learning actions.

The time to act on these priorities is now. UNESCO, UNICEF, and the World Bank are partnering to help drive that action.

Last Updated: Mar 30, 2021

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Coronavirus and schools: Reflections on education one year into the pandemic

Subscribe to the center for universal education bulletin, daphna bassok , daphna bassok nonresident senior fellow - governance studies , brown center on education policy lauren bauer , lauren bauer fellow - economic studies , associate director - the hamilton project stephanie riegg cellini , stephanie riegg cellini nonresident senior fellow - governance studies , brown center on education policy helen shwe hadani , helen shwe hadani former brookings expert michael hansen , michael hansen senior fellow - brown center on education policy , the herman and george r. brown chair - governance studies douglas n. harris , douglas n. harris nonresident senior fellow - governance studies , brown center on education policy , professor and chair, department of economics - tulane university brad olsen , brad olsen senior fellow - global economy and development , center for universal education richard v. reeves , richard v. reeves president - american institute for boys and men jon valant , and jon valant director - brown center on education policy , senior fellow - governance studies kenneth k. wong kenneth k. wong nonresident senior fellow - governance studies , brown center on education policy.

March 12, 2021

• 11 min read

One year ago, the World Health Organization declared the spread of COVID-19 a worldwide pandemic. Reacting to the virus, schools at every level were sent scrambling. Institutions across the world switched to virtual learning, with teachers, students, and local leaders quickly adapting to an entirely new way of life. A year later, schools are beginning to reopen, the $1.9 trillion stimulus bill has been passed, and a sense of normalcy seems to finally be in view; in President Joe Biden’s speech last night, he spoke of “finding light in the darkness.” But it’s safe to say that COVID-19 will end up changing education forever, casting a critical light on everything from equity issues to ed tech to school financing.

Below, Brookings experts examine how the pandemic upended the education landscape in the past year, what it’s taught us about schooling, and where we go from here.

In the United States, we tend to focus on the educating roles of public schools, largely ignoring the ways in which schools provide free and essential care for children while their parents work. When COVID-19 shuttered in-person schooling, it eliminated this subsidized child care for many families. It created intense stress for working parents, especially for mothers who left the workforce at a high rate.

The pandemic also highlighted the arbitrary distinction we make between the care and education of elementary school children and children aged 0 to 5 . Despite parents having the same need for care, and children learning more in those earliest years than at any other point, public investments in early care and education are woefully insufficient. The child-care sector was hit so incredibly hard by COVID-19. The recent passage of the American Rescue Plan is a meaningful but long-overdue investment, but much more than a one-time infusion of funds is needed. Hopefully, the pandemic represents a turning point in how we invest in the care and education of young children—and, in turn, in families and society.

Congressional reauthorization of Pandemic EBT for  this school year , its  extension  in the American Rescue Plan (including for summer months), and its place as a  central plank  in the Biden administration’s anti-hunger agenda is well-warranted and evidence based. But much more needs to be done to ramp up the program–even  today , six months after its reauthorization, about half of states do not have a USDA-approved implementation plan.

In contrast, enrollment is up in for-profit and online colleges. The research repeatedly finds weaker student outcomes for these types of institutions relative to community colleges, and many students who enroll in them will be left with more debt than they can reasonably repay. The pandemic and recession have created significant challenges for students, affecting college choices and enrollment decisions in the near future. Ultimately, these short-term choices can have long-term consequences for lifetime earnings and debt that could impact this generation of COVID-19-era college students for years to come.

Many U.S. educationalists are drawing on the “build back better” refrain and calling for the current crisis to be leveraged as a unique opportunity for educators, parents, and policymakers to fully reimagine education systems that are designed for the 21st rather than the 20th century, as we highlight in a recent Brookings report on education reform . An overwhelming body of evidence points to play as the best way to equip children with a broad set of flexible competencies and support their socioemotional development. A recent article in The Atlantic shared parent anecdotes of children playing games like “CoronaBall” and “Social-distance” tag, proving that play permeates children’s lives—even in a pandemic.

Tests play a critical role in our school system. Policymakers and the public rely on results to measure school performance and reveal whether all students are equally served. But testing has also attracted an inordinate share of criticism, alleging that test pressures undermine teacher autonomy and stress students. Much of this criticism will wither away with  different  formats. The current form of standardized testing—annual, paper-based, multiple-choice tests administered over the course of a week of school—is outdated. With widespread student access to computers (now possible due to the pandemic), states can test students more frequently, but in smaller time blocks that render the experience nearly invisible. Computer adaptive testing can match paper’s reliability and provides a shorter feedback loop to boot. No better time than the present to make this overdue change.

A third push for change will come from the outside in. COVID-19 has reminded us not only of how integral schools are, but how intertwined they are with the rest of society. This means that upcoming schooling changes will also be driven by the effects of COVID-19 on the world around us. In particular, parents will be working more from home, using the same online tools that students can use to learn remotely. This doesn’t mean a mass push for homeschooling, but it probably does mean that hybrid learning is here to stay.

I am hoping we will use this forced rupture in the fabric of schooling to jettison ineffective aspects of education, more fully embrace what we know works, and be bold enough to look for new solutions to the educational problems COVID-19 has illuminated.

There is already a large gender gap in education in the U.S., including in  high school graduation rates , and increasingly in college-going and college completion. While the pandemic appears to be hurting women more than men in the labor market, the opposite seems to be true in education.

Looking through a policy lens, though, I’m struck by the timing and what that timing might mean for the future of education. Before the pandemic, enthusiasm for the education reforms that had defined the last few decades—choice and accountability—had waned. It felt like a period between reform eras, with the era to come still very unclear. Then COVID-19 hit, and it coincided with a national reckoning on racial injustice and a wake-up call about the fragility of our democracy. I think it’s helped us all see how connected the work of schools is with so much else in American life.

We’re in a moment when our long-lasting challenges have been laid bare, new challenges have emerged, educators and parents are seeing and experimenting with things for the first time, and the political environment has changed (with, for example, a new administration and changing attitudes on federal spending). I still don’t know where K-12 education is headed, but there’s no doubt that a pivot is underway.

• First, state and local leaders must leverage commitment and shared goals on equitable learning opportunities to support student success for all.
• Second, align and use federal, state, and local resources to implement high-leverage strategies that have proven to accelerate learning for diverse learners and disrupt the correlation between zip code and academic outcomes.
• Third, student-centered priority will require transformative leadership to dismantle the one-size-fits-all delivery rule and institute incentive-based practices for strong performance at all levels.
• Fourth, the reconfigured system will need to activate public and parental engagement to strengthen its civic and social capacity.
• Finally, public education can no longer remain insulated from other policy sectors, especially public health, community development, and social work.

These efforts will strengthen the capacity and prepare our education system for the next crisis—whatever it may be.

Higher Education K-12 Education

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Harvard Staff Writer

Paul Reville says COVID-19 school closures have turned a spotlight on inequities and other shortcomings

This is part of our Coronavirus Update series in which Harvard specialists in epidemiology, infectious disease, economics, politics, and other disciplines offer insights into what the latest developments in the COVID-19 outbreak may bring.

As former secretary of education for Massachusetts, Paul Reville is keenly aware of the financial and resource disparities between districts, schools, and individual students. The school closings due to coronavirus concerns have turned a spotlight on those problems and how they contribute to educational and income inequality in the nation. The Gazette talked to Reville, the Francis Keppel Professor of Practice of Educational Policy and Administration at Harvard Graduate School of Education , about the effects of the pandemic on schools and how the experience may inspire an overhaul of the American education system.

Paul Reville

GAZETTE: Schools around the country have closed due to the coronavirus pandemic. Do these massive school closures have any precedent in the history of the United States?

REVILLE: We’ve certainly had school closures in particular jurisdictions after a natural disaster, like in New Orleans after the hurricane. But on this scale? No, certainly not in my lifetime. There were substantial closings in many places during the 1918 Spanish Flu, some as long as four months, but not as widespread as those we’re seeing today. We’re in uncharted territory.

GAZETTE: What lessons did school districts around the country learn from school closures in New Orleans after Hurricane Katrina, and other similar school closings?

REVILLE:   I think the lessons we’ve learned are that it’s good [for school districts] to have a backup system, if they can afford it. I was talking recently with folks in a district in New Hampshire where, because of all the snow days they have in the wintertime, they had already developed a backup online learning system. That made the transition, in this period of school closure, a relatively easy one for them to undertake. They moved seamlessly to online instruction.

Most of our big systems don’t have this sort of backup. Now, however, we’re not only going to have to construct a backup to get through this crisis, but we’re going to have to develop new, permanent systems, redesigned to meet the needs which have been so glaringly exposed in this crisis. For example, we have always had large gaps in students’ learning opportunities after school, weekends, and in the summer. Disadvantaged students suffer the consequences of those gaps more than affluent children, who typically have lots of opportunities to fill in those gaps. I’m hoping that we can learn some things through this crisis about online delivery of not only instruction, but an array of opportunities for learning and support. In this way, we can make the most of the crisis to help redesign better systems of education and child development.

GAZETTE: Is that one of the silver linings of this public health crisis?

REVILLE: In politics we say, “Never lose the opportunity of a crisis.” And in this situation, we don’t simply want to frantically struggle to restore the status quo because the status quo wasn’t operating at an effective level and certainly wasn’t serving all of our children fairly. There are things we can learn in the messiness of adapting through this crisis, which has revealed profound disparities in children’s access to support and opportunities. We should be asking: How do we make our school, education, and child-development systems more individually responsive to the needs of our students? Why not construct a system that meets children where they are and gives them what they need inside and outside of school in order to be successful? Let’s take this opportunity to end the “one size fits all” factory model of education.

GAZETTE: How seriously are students going to be set back by not having formal instruction for at least two months, if not more?

“The best that can come of this is a new paradigm shift in terms of the way in which we look at education, because children’s well-being and success depend on more than just schooling,” Paul Reville said of the current situation. “We need to look holistically, at the entirety of children’s lives.”

Stephanie Mitchell/Harvard file photo

REVILLE: The first thing to consider is that it’s going to be a variable effect. We tend to regard our school systems uniformly, but actually schools are widely different in their operations and impact on children, just as our students themselves are very different from one another. Children come from very different backgrounds and have very different resources, opportunities, and support outside of school. Now that their entire learning lives, as well as their actual physical lives, are outside of school, those differences and disparities come into vivid view. Some students will be fine during this crisis because they’ll have high-quality learning opportunities, whether it’s formal schooling or informal homeschooling of some kind coupled with various enrichment opportunities. Conversely, other students won’t have access to anything of quality, and as a result will be at an enormous disadvantage. Generally speaking, the most economically challenged in our society will be the most vulnerable in this crisis, and the most advantaged are most likely to survive it without losing too much ground.

GAZETTE: Schools in Massachusetts are closed until May 4. Some people are saying they should remain closed through the end of the school year. What’s your take on this?

REVILLE: That should be a medically based judgment call that will be best made several weeks from now. If there’s evidence to suggest that students and teachers can safely return to school, then I’d say by all means. However, that seems unlikely.

GAZETTE: The digital divide between students has become apparent as schools have increasingly turned to online instruction. What can school systems do to address that gap?

REVILLE: Arguably, this is something that schools should have been doing a long time ago, opening up the whole frontier of out-of-school learning by virtue of making sure that all students have access to the technology and the internet they need in order to be connected in out-of-school hours. Students in certain school districts don’t have those affordances right now because often the school districts don’t have the budget to do this, but federal, state, and local taxpayers are starting to see the imperative for coming together to meet this need.

Twenty-first century learning absolutely requires technology and internet. We can’t leave this to chance or the accident of birth. All of our children should have the technology they need to learn outside of school. Some communities can take it for granted that their children will have such tools. Others who have been unable to afford to level the playing field are now finding ways to step up. Boston, for example, has bought 20,000 Chromebooks and is creating hotspots around the city where children and families can go to get internet access. That’s a great start but, in the long run, I think we can do better than that. At the same time, many communities still need help just to do what Boston has done for its students.

Communities and school districts are going to have to adapt to get students on a level playing field. Otherwise, many students will continue to be at a huge disadvantage. We can see this playing out now as our lower-income and more heterogeneous school districts struggle over whether to proceed with online instruction when not everyone can access it. Shutting down should not be an option. We have to find some middle ground, and that means the state and local school districts are going to have to act urgently and nimbly to fill in the gaps in technology and internet access.

GAZETTE : What can parents can do to help with the homeschooling of their children in the current crisis?

“In this situation, we don’t simply want to frantically struggle to restore the status quo because the status quo wasn’t operating at an effective level and certainly wasn’t serving all of our children fairly.”

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REVILLE: School districts can be helpful by giving parents guidance about how to constructively use this time. The default in our education system is now homeschooling. Virtually all parents are doing some form of homeschooling, whether they want to or not. And the question is: What resources, support, or capacity do they have to do homeschooling effectively? A lot of parents are struggling with that.

And again, we have widely variable capacity in our families and school systems. Some families have parents home all day, while other parents have to go to work. Some school systems are doing online classes all day long, and the students are fully engaged and have lots of homework, and the parents don’t need to do much. In other cases, there is virtually nothing going on at the school level, and everything falls to the parents. In the meantime, lots of organizations are springing up, offering different kinds of resources such as handbooks and curriculum outlines, while many school systems are coming up with guidance documents to help parents create a positive learning environment in their homes by engaging children in challenging activities so they keep learning.

There are lots of creative things that can be done at home. But the challenge, of course, for parents is that they are contending with working from home, and in other cases, having to leave home to do their jobs. We have to be aware that families are facing myriad challenges right now. If we’re not careful, we risk overloading families. We have to strike a balance between what children need and what families can do, and how you maintain some kind of work-life balance in the home environment. Finally, we must recognize the equity issues in the forced overreliance on homeschooling so that we avoid further disadvantaging the already disadvantaged.

GAZETTE: What has been the biggest surprise for you thus far?

REVILLE: One that’s most striking to me is that because schools are closed, parents and the general public have become more aware than at any time in my memory of the inequities in children’s lives outside of school. Suddenly we see front-page coverage about food deficits, inadequate access to health and mental health, problems with housing stability, and access to educational technology and internet. Those of us in education know these problems have existed forever. What has happened is like a giant tidal wave that came and sucked the water off the ocean floor, revealing all these uncomfortable realities that had been beneath the water from time immemorial. This newfound public awareness of pervasive inequities, I hope, will create a sense of urgency in the public domain. We need to correct for these inequities in order for education to realize its ambitious goals. We need to redesign our systems of child development and education. The most obvious place to start for schools is working on equitable access to educational technology as a way to close the digital-learning gap.

GAZETTE: You’ve talked about some concrete changes that should be considered to level the playing field. But should we be thinking broadly about education in some new way?

REVILLE: The best that can come of this is a new paradigm shift in terms of the way in which we look at education, because children’s well-being and success depend on more than just schooling. We need to look holistically, at the entirety of children’s lives. In order for children to come to school ready to learn, they need a wide array of essential supports and opportunities outside of school. And we haven’t done a very good job of providing these. These education prerequisites go far beyond the purview of school systems, but rather are the responsibility of communities and society at large. In order to learn, children need equal access to health care, food, clean water, stable housing, and out-of-school enrichment opportunities, to name just a few preconditions. We have to reconceptualize the whole job of child development and education, and construct systems that meet children where they are and give them what they need, both inside and outside of school, in order for all of them to have a genuine opportunity to be successful.

Within this coronavirus crisis there is an opportunity to reshape American education. The only precedent in our field was when the Sputnik went up in 1957, and suddenly, Americans became very worried that their educational system wasn’t competitive with that of the Soviet Union. We felt vulnerable, like our defenses were down, like a nation at risk. And we decided to dramatically boost the involvement of the federal government in schooling and to increase and improve our scientific curriculum. We decided to look at education as an important factor in human capital development in this country. Again, in 1983, the report “Nation at Risk” warned of a similar risk: Our education system wasn’t up to the demands of a high-skills/high-knowledge economy.

We tried with our education reforms to build a 21st-century education system, but the results of that movement have been modest. We are still a nation at risk. We need another paradigm shift, where we look at our goals and aspirations for education, which are summed up in phrases like “No Child Left Behind,” “Every Student Succeeds,” and “All Means All,” and figure out how to build a system that has the capacity to deliver on that promise of equity and excellence in education for all of our students, and all means all. We’ve got that opportunity now. I hope we don’t fail to take advantage of it in a misguided rush to restore the status quo.

This interview has been condensed and edited for length and clarity.

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The impact of Covid-19 on student achievement: Evidence from a recent meta-analysis ☆

Giorgio di pietro.

a European Commission- Joint Research Centre 1 , Edificio Expo, Calle Inca Garcilaso, 3, 41092, Seville, Spain

b Institute of Labour Economics (IZA), Schaumburg-Lippe-Straße 5-9, 53113, Bonn, Germany

Associated Data

Data will be made available on request.

This work attempts to synthetize existing research about the impact of Covid-19 school closure on student achievement. It extends previous systematic reviews and meta-analyses by (a) using a more balanced sample in terms of country composition, (b) considering new moderators (type of data and research design), and (c) including studies on tertiary education students in addition to primary and secondary education students. Our meta-analysis findings show that the pandemic had, on average, a detrimental effect on learning. The magnitude of this learning deficit (about 0.19 standard deviations of student achievement) appears to be roughly comparable to that suffered by students who have experienced a significant disruption in their schooling due to a major natural disaster (e.g., Hurricane Katrina). Students are also found to have lost more ground in math/science than in other subjects. Additionally, one year or more after the first lockdown, students seem to have been unable to catch up on unfinished learning from the pandemic. This result suggests that more efforts should be made to ensure students recover their missed learning in order to avoid negative long-term consequences for them and society.

• • We perform a meta-analysis to study the effect of Covid-19 on student achievement.
• • Our dataset includes 239 estimates from 39 studies covering 19 countries.
• • The pandemic had an overall negative effect on learning outcomes.
• • Students lost more ground in math/science than in other subjects.
• • One year or more after Covid-19 students have not recovered from the initial learning loss.

1. Introduction

The Covid-19 pandemic caused a major disruption in the schooling system around the world. In most countries, educational institutions had to close for several weeks or months in an attempt to reduce the spread of the virus ( UNESCO, 2020a ). Students had to continue their schooling from home using different learning tools such as video conferencing, radio and TV. However, the outbreak of Covid-19 was so sudden that there was little or no time for many schools to design and implement learning programs specifically designed to support children's learning while at home. A significant proportion of teachers were unprepared for online learning as they lacked appropriate pedagogical and digital skills ( School Education Gateway, 2020 ). Similarly, many students also struggled to adjust to the new format of learning. In addition to problems in accessing appropriate technology (computers, reliable internet connection, etc.), not all students had a home environment free of disturbances and distractions, hence conducive to learning ( Pokhrel & Chhetri, 2021 ). A large number of parents had serious difficulties in combining their work responsibilities (if not joblessness) with looking after and educating their children ( Soland et al., 2020 ). Moreover, there is evidence showing that Covid-19 and the related containment measures have had a detrimental effect on children's wellbeing ( Xie et al., 2020 ). Longer periods of social isolation might have adversely affected students' mental health (e.g., anxiety and depression) and physical activity ( Vaillancourt et al., 2021 ). This is also likely to have contributed to negatively impact their academic performance given the close association between mental and physical health and educational outcomes ( Joe et al., 2009 ).

While in the literature there is already a relatively large consensus that student learning suffered a setback due to Covid-19, as pointed out by several researchers (e.g., Donnelly & Patrinos, 2022 ; Patrinos et al., 2022 ), more research in this area is still needed. Findings from new studies are important given that, as stated in a recent article published in the World Economic Forum, the full scale of the impact of the pandemic on the education of children is “only just starting to emerge” ( Broom, 2022 ). Not only is a better understanding of the educational impact of Covid-19 needed, but special attention should be paid to investigate the legacy effects of the pandemic. As argued in several papers (e.g., Hanushek & Woessmann, 2020 ; Psacharopoulos et al., 2021 ), there is the risk that the disruption in learning caused by Covid-19 may persist over time, having long-term consequences on students’ knowledge and skills as well as on their labour market prospects. It is therefore very important to determine if and to what extent those children whose schooling was disrupted by Covid-19 subsequently got back on track and reduced their learning deficits. 2 Similarly, it is relevant to gain a more solid understanding of how the educational impact of Covid-19 varies across students and circumstances. This would help educators and policymakers identify those groups of students who may need extra support to recover from the learning deficit caused by the pandemic.

This paper uses meta-analysis in an attempt to synthetize and harmonize evidence about the effect of Covid-19 school closures on student learning outcomes. Meta-analysis, which is widely employed in education as well as in other fields, combines the findings of multiple studies in order to provide a more precise estimate of the relevant effect size and explain the heterogeneity of the results that have been found in individual studies. A total of 239 separate estimates from 39 studies are considered. We extend previous systematic reviews and meta-analyses 3 in four main ways. First, compared to previous meta-analyses, this study covers a larger number of countries (i.e., 19). Not only are several new countries considered in the analysis (e.g., Slovenia, Egypt), but US and UK studies do not dominate the collected empirical evidence. For instance, while in Betthäuser et al. (2023) about 71.1% of the effect sizes are derived from these studies, in our paper the corresponding figure is approximately 33.9%. 4 This makes our results of more general relevance. 5 Second, the current meta-analysis adds to previous meta-analyses by including also studies looking at the impact of Covid-19 among tertiary education students in addition to primary and secondary education students. This is important because, as individuals progress through the education system, academic challenges increase and so does the pressure to perform well. Several studies from various countries (e.g., Bratti et al., 2004 ; Dabalen et al., 2001 ; Koda & Yuki, 2013 ) show that the final grade awarded to students successfully completing university is an important predictor of their labour market prospects. Third, while some relevant moderator variables have already been noted (e.g., subject, level of education, geographical area), the present meta-analysis adds several new ones including type of data and research design. The relevance of these factors in explaining the heterogeneity of results across studies is well-known in the meta-analysis literature. For instance, Havránek et al. (2020) indicate that researchers conducting meta-regression analysis in economics should consider data types. Similarly, Stanley and Jarrell (1989) suggest that variables capturing differences in methodology need to be included among moderators in meta-regression models. More in general, moderators are situational variables as well as characteristics of studies that might influence the effect estimate ( Judd, 2015 ). Fourth, in contrast to previous similar meta-analyses (e.g., König & Frey, 2022 ), we look closely at the issue of the specification of the meta-regression model. As observed by Stanley and Doucouliagos (2012) , this is a more relevant problem in meta-analysis than in primary econometric studies given the higher risk of exhausting degrees of freedom in the former than in the latter. Following recent literature (e.g., Di Pietro, 2022 ), we employ different methods to select the moderator variables to be included in the meta-regression model.

The remainder of the paper is set as follows. Section 2 describes the process of selecting studies and collecting data. It also discusses the empirical approach and the possibility of publication bias. Section 3 reports and discusses the empirical results. Section 4 concludes.

To perform this meta-analysis, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) ( Moher et al., 2009 ).

2.1. Inclusion criteria

With the purpose of this study in mind, a set of inclusion criteria was defined. They guided the selection of the studies included in this meta-analysis. Specifically, the following four inclusion criteria were used:

• ● the study should quantitatively examine the effect of Covid-19 on student achievement in primary, secondary or tertiary education. This means that the data used in this study were collected before and during the pandemic (or only during the pandemic if, when schools were closed, some students were still receiving in-person teaching thereby simulating pre-pandemic conditions), therefore clearly distinguishing between a control and a treated group, respectively.
• ● the study should use objective indicators (e.g., test scores) to measure student achievement.
• ● the study should be based on real data.
• ● the study should report data on an effect size (or sufficient information to compute it) and its standard error (or t -statistic, or p -value, or sufficient information to calculate it).

2.2. Search ad screening procedures

To identify the relevant studies, we searched in six different electronic databases (i.e., Google Scholar, 6 EconLit, ScienceDirect, Education Resources Information Center, JSTOR and Emerald). The following keywords were used: “Covid-19 (OR coronavirus OR pandemic OR Cov) AND student (OR academic OR scholastic) performance (OR achievement OR learning OR outcome) OR test score”.

This search, which ended on 15 th July 2022, delivered 6,075 hits. 717 duplicates were removed. We kept updated or published versions of any working paper we found. Next, the titles and the abstracts of the remaining 5,358 records were assessed. Following this, 5,205 studies were excluded as they use qualitative approaches (e.g., interviews), report teachers'/parents’ views about the educational impact of Covid-19 (e.g., Kim et al., 2022 ; Lupas et al., 2021 ), or provide a theoretical discussion about how the pandemic is likely to affect education (e.g., Di Pietro et al., 2020 ). Similarly, studies containing predictions and/or projections were also removed (e.g., Kuhfeld et al., 2020a ). After this initial screening, the content of the remaining 153 studies was carefully examined, and only those fulfilling all the inclusion criteria were considered. In this phase, we excluded studies that, although attempting to understand how the pandemic impacted student learning, employ a different outcome measure (e.g., dropout rate) than the one considered in this meta-analysis (e.g., Tsolou et al., 2021 ). In the same vein, we removed studies using student self-reported outcome measures as well as those examining the educational impact of Covid-19 on specific subgroups of students (e.g., Agostinelli et al., 2022 ). Finally, in order to ensure that key sources were not missed, we also screened the references included in previous meta-analyses and systematic reviews. Two more relevant articles were identified through this search. A total of 39 studies was included in this study. Fig. 1 summarizes the literature search and the screening procedure.

Flow chart of the search and screening process.

While all the titles and abstracts were screened only by the author, the next stages of the study selection process were carried out by the author and by another researcher who independently classified the studies as relevant and irrelevant based on the predefined inclusion criteria. While the inter-rater agreement was very high (i.e., 97%), studies on which there was disagreement were discussed in depth until consensus was reached.

2.3. Study coding

All the studies included in this meta-analysis were read in-depth, and relevant information and findings were extracted. Study coding was performed following the same procedure used for the final stages of the study selection process. The inter-rater agreement was again high (i.e., 93%).

In line with the current best practice in meta-analysis ( Polák, 2019 ), we use all relevant estimates included in the selected studies. As argued by Cheung (2019) , not doing so results in missed opportunities to take advantage of all the available data to answer the research question/s under investigation. However, a fundamental issue with this approach lies in the dependence between multiple estimates from the same study given that effect sizes are assumed to be independent in meta-analysis ( Cheung & Vijayakumar, 2016 ). As discussed later in the paper, several methods are used to account for within-study dependence.

2.3.1. Effect size calculation

In order to be able to aggregate the various impact estimates reported in the selected studies, one needs to convert them into a common metric. Consistent with previous relevant systematic reviews and meta-analyses, we use the Cohen's d as a scale-free effect size measure. Cohen's d refers to standardised mean differences and is calculated by dividing the mean difference in student performance between pre-Covid and Covid periods by the pooled standard deviation. While in some cases the Cohen's d was retrieved from the studies, in others it was calculated using information directly available from them. Where the latter was not possible, the studies' author/s was/were contacted to obtain the relevant data. If not reported, the Cohen's d standard error was computed using the formula given in Cooper and Hedges (1994) . In case no information on sample sizes were available from the studies but exact p -values were instead reported, the formula provided by Higgins and Green (2011) was employed to obtain standard errors. In some instances, we also used information on effect sizes contained in the electronic supplement of the meta-analysis article by König and Frey (2022) . For instance, this was the case when a study does not report Cohen's d but this information has been already collected by König and Frey who have contacted the relevant author/s.

2.3.2. Moderator variables

For each effect size, we code several moderator variables, that is, factors potentially influencing the size of the effect of Covid-19 on student achievement. These moderator variables can be divided into two categories: 1) context and 2) characteristics. Regarding the former, we consider:

a) The level of education. Several arguments suggest that remote schooling is more challenging for younger students compared to their older counterparts. To start with, younger learners are less likely to have access, and be able to independently use digital devices. They may be unable to sign into an online class without assistance, may need help or supervision to perform an online task, and may more easily get distracted. Parental engagement therefore plays a crucial role in the success of younger pupils in an online learning environment. However, even though critical, the supervision required for online schooling while younger children are at home may turn out to be unsustainable for many parents who are at the same time engaged with remote working ( Lucas et al., 2020 ). There is also evidence showing that younger students are less likely to have a quiet space to work at home than their older peers. For instance, Andrew et al. (2020) found that in the UK during the first Covid-19 lockdown while the proportion of primary school students reporting not to have a designated space to study at home was about 20%, the corresponding figure for secondary school students was approximately 10%. Furthermore, children in early grades may especially miss in person teaching as they depend on situational learning ( Storey & Zhang, 2021b ). A great emphasis is placed on relationships and interactions with others in order to acquire knowledge. Younger learners are also more likely to need movement and exploration, and these are things that one cannot do while sitting at home and looking at a screen ( Hinton, 2020 ). Finally, some studies ( Domínguez-Álvarez et al., 2020 ; Gómez-Becerra et al., 2020 ) showed that during Covid-19 younger children present more emotional problems than older children. Tomasik et al. (2021) argued that the former group are more likely to have difficulties in coping with socio-emotional stressors associated with the pandemic. Perhaps also as a result of this, there was greater attention to pastoral care than curriculum coverage among primary school students, as opposed to secondary school students ( Julius & Sims, 2020 ).

In an attempt to investigate how the educational impact of the pandemic varies across student age groups, we distinguish between primary, secondary, and tertiary education students.

b) Subject. It is often claimed that the effect of the pandemic on student achievement varies depending on the subject being assessed. Specifically, three main arguments have been advanced to suggest that the pandemic has made students lose more ground in math than in other subjects.

First, while the Covid-19 lockdown has called for increased parental involvement in their children's learning, parents often feel they have difficulties in assisting their children in math. Panaoura (2020) looked at parents' perception of how they have helped their children in math learning during the pandemic in Cyprus. She found that parents' lack of confidence or their low self-efficacy beliefs were enhanced during this period. More teachers' guidance and training would have been needed. Using data on Chinese primary school students during Covid-19, Wang et al. (2022) concluded that parental involvement had a positive impact on children's achievement in Chinese and English, but not in math. While parents are likely to be knowledgeable about the learning content of Chinese and English lessons, this may not be the case for math lessons. In daily life, language practice is more used than math practice. Furthermore, parents may be familiar with math methods different from the ones used by teachers ( Shanley, 2016 ).

Second, teaching math in a fully online context is very challenging. Using data from a survey addressed to math lecturers between May and June 2020, Ní Fhloinn and Fitzmaurice (2021) found that most of the respondents agreed that it is harder to teach math remotely. This is partly due to the idiosyncratic nature of this discipline. It is especially difficult for math instructors to adapt their teaching style to online learning conditions. While many of them used to handwrite the material in real time during their lectures, only a small proportion have the technology to continue doing so online. On the other hand, also students may have problems in communicating math online. Not only do students need to learn and accustom themselves to use technology in order to write mathematical symbols, but this is not always possible in online platforms such as chats ( Mullen et al., 2021 ). Online engagement in math is particularly difficult. Involving students in online discussions around an exact science like math may turn out to be very challenging.

Third, the economic and health problems caused by Covid-19 coupled with the sudden shift to online learning are likely to have increased math anxiety among students. This can be defined as a negative emotional reaction that interferes with the solving of math problems ( Blazer, 2011 , p. 1102). Math anxiety prevents students from learning math because it leads to low self-esteem, frustration, and anger ( Fennema & Sherman, 1976 ). Mamolo (2022) found that the students’ math motivation and self-efficacy decreased during the pandemic. Similarly, Mendoza et al. (2021) and Arnal-Palacián et al. (2022) provided evidence about higher levels of math anxiety experienced by university and primary school students, respectively, during Covid-19.

In light of the above, subjects have been grouped into three different broad categories: math/science, humanities, and a mix category.

c) Timing of student assessment during Covid-19 . As stated earlier, an important question is the extent to which the pandemic has long-lasting effects on learning outcomes. Several arguments suggest that the negative effect of Covid-19 on student achievement may decline as we move to a later stage of the pandemic. To start with, a number of provisions are likely to have been taken in order to help students catch up after the first lockdown and following the re-opening of schools (at least temporarily). An UNESCO, UNICEF, World Bank and OECD report (2021) showed that in the third quarter of 2020 many countries around the world were planning to adopt support programs with the aim of reducing the learning deficit suffered by students earlier in the year. These programs include increased in-person class time, remedial programs, and accelerate learning schemes. Additionally, one would expect students and their parents to have become more used to remote learning during successive school closures and periods of online classes. Finally, many teachers and schools have probably learned important lessons from the first lockdown. These lessons might have helped them design and implement more effective remote learning measures in the subsequent phases of the pandemic.

However, despite the aforementioned considerations, it is possible that it will take some time before students are able to recover from the learning deficit caused by Covid-19. Students may experience problems in re-engaging with education activities following the re-opening of schools. There is evidence showing that, after several months of remote schooling, students have become more passive ad feel disengaged from their learning ( Toth, 2021 ). The stress and anxiety stemming from the pandemic are likely to have caused a fall in student motivation and morale. The uncertainty of the learning environment under Covid-19 could have also contributed to reduce students’ educational aspirations ( OECD, 2020 ). Additionally, during the academic year 2021–2022, as a result of successive waves and different variants of Covid-19, schools had to face several problems including significant staff shortages, high rates of absenteeism and sickness, and rolling school closures ( Kuhfeld & Lewis, 2022 ). Evidence from the US shows that the pandemic has aggravated the problem of teacher shortage ( Schmitt & deCourcy, 2022 ). Following school re-opening, teachers faced new requirements (e.g., hybrid teaching, more administrative tasks) that added to their already full workloads prior to Covid-19 ( Pressley, 2022 ). This increased their stress levels, which made them more likely to leave their job. While many teachers have quit their job during the pandemic, this reduction in staff has not been fully offset by new hires.

In an attempt to look at how the educational impact of Covid-19 changes over time, we distinguish whether the student learning outcome was assessed in 2020 or 2021.

d) The geographical area where the study takes place. We make a distinction between Europe (i.e., Belgium, Czech Republic, Denmark, Germany, Italy, Netherlands, Norway, Spain, Sweden, Slovenia, Switzerland and the UK) and non-Europe (i.e., Australia, Brazil, China, Egypt, Mexico, South Africa and the US).

Coming to 2) characteristics, we code:

e) the type of data . We distinguish between cross-sectional and longitudinal data. As noted by Werner and Woessman (2021), cross-sectional data do not allow to separate the Covid-19 effect from the cohort effects. Using this type of data, the performance of a cohort of students who have been affected by Covid-19 school closures is typically compared to the performance of a previous cohort of students who took the same test in a pre-Covid-19 period. However, this approach does not take into account the possibility that other factors influencing student achievement (e.g., change in education policies) might have changed coincidentally at the same time as Covid-19. Student-level longitudinal (panel) data help to address the cohort effects bias. They allow to look at changes in student performance before and after the lockdown and compare them with the progress made by similar students over the same period of previous years.

f) the type of research design . A number of different methodologies have been used in an attempt to identify the effect of Covid-19 school closures on academic achievement. In this study, we code the type of research design into the following three categories: descriptive, correlational, and quasi experimental/experimental ( Locke et al., 2010 ). Studies using a descriptive research design (e.g., Moliner & Alegre, 2022 ) provide information about the average gap in test scores between the Covid-19 and non-Covid-19 cohorts without accounting for differences between these two cohorts (for example in terms of individual characteristics such as gender and socio-economic background) that could affect academic performances. 7 On the other hand, studies employing a correlational research design (e.g., Ludewig et al., 2022 ) attempt to isolate the effect of Covid-19 from that associated with other factors that could influence student achievement, but their results cannot be given a causal interpretation. Finally, studies using a quasi-experimental or experimental design (e.g., Engzell et al., 2021 ) move closer to a causal interpretation of the relationship between Covid-19 and student performance.

g) the publication year . This study characteristic is a typical moderator variable in meta-analyses. It controls for time-trend effects ( Schütt, 2021 ). In line with the approach followed by several recent meta-analyses (see, for instance, Di Pietro, 2022 ), we consider the year of the first appearance of a draft of the study in Google Scholar. This measure is preferred to publication year on the ground that journals significantly differ with respect to the time between online availability date of an article and the date when the article is given a volume and issue number 8 ( Al & Soydal, 2017 ). Additionally, in our dataset, there are two journal articles that are only available online and it is unclear in which issue of the journal they will be published. The publication years considered are: 2020, 2021, and 2022.

h) the type of publication. This moderator variable is considered in an attempt to control for the quality of the studies included in our sample. We distinguish between journal articles and other publication formats. Articles published in journals are expected to be of higher scientific rigour since they are more likely to have gone through a review process. Additionally, non-journal articles are more likely to contain typos in their regression tables ( Cazachevici et al., 2020 ).

Finally, consistent with the approach taken in several studies (e.g., de Linde Leonard & Stanley, 2020 ), i) the effect size's standard error is also included among our moderator variables.

2.4. Sample characteristics

The dataset used for the meta-analysis includes a total of 239 different impact estimates extracted from 39 separate studies. Each study included in the dataset contains a number of estimates that vary from 1 to 32. Several reasons explain why most studies (i.e., 79%) reported multiple estimates. Many studies (e.g., Bielinski et al., 2021 ; Borgonovi & Ferrara, 2022 ; Feng et al., 2021 ; Gambi & De Witte, 2021 ; Maldonado & De Witte, 2022 ) estimated the effect of Covid-19 on student performance in several subjects. Similarly, a large number of studies (e.g., Ardington et al., 2021 ; Contini et al., 2021 ; Domingue et al., 2021 ; Gore et al., 2021 ) examined the impact of the pandemic on the achievement of students of different levels of education or even of students of different grades within the same level of education. For instance, Meeter (2021) analysed how Covid-19 affected the math performance of primary school children of grades 2–6. Some studies also provided different estimates showing both the short and long-term effects of Covid-19 on student achievement. For example, Kuhfeld et al. (2022) looked at changes in student test scores in fall 2020 and fall 2021 relative to fall 2019.

Table 1 presents the studies included in the dataset. Studies are listed alphabetically. For each study, we report information on the author(s), year of publication, 9 country examined, type of test used to measure student performance, number of the effect sizes collected and their mean value. 10 The studies cover a total of 19 countries. The largest source countries are the US (71 estimates), Germany (39 estimates) and Belgium (33 estimates).

Sources for meta-analysis.

Table 2 shows the descriptive statistics of the moderator variables used in the meta-regressions. While Column (1) displays simple averages (and standard deviations), Column (2) reports averages (and standard deviations) weighted by the inverse of the number of estimates reported in each study. Column (3) reports the number of effect sizes for each moderator variable.

Descriptive statistics.

2.5. Risk of bias assessment

In line with the approach adopted by Betthäuser et al. (2023) and Hammerstein et al. (2021) , the risk of bias in nonrandomized studies was assessed in 38 11 studies using the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool ( Sterne et al., 2016 ). Each study was independently evaluated by the author and another researcher, and any disagreements were resolved through discussion to reach a consensus. Studies were scored on six different domains: confounding, participant selection, classification of interventions, missing data, measurement of outcomes, and reporting bias. 12

Table 3 shows the risk of bias ratings for each domain (as well as an overall judgement) for the 38 studies. The lack of appropriate methods to control for confounders, sample selection problems and missing data appear to be the most common sources of potential bias. In several studies, vulnerable students, who have been among the most hardly hit by the pandemic, tend to be under-represented in the Covid-19 sample. This may lead to an underestimation of the pandemic-related learning delays. For example, the study by Gambi and De Witte (2021) relies on a sample where schools participating in the 2021 survey have a more advantaged student population in terms of neighbourhood of residence and mother's education, and have a smaller fraction of students that are considered to be slow learners. Similarly, in the longitudinal data used by Ardington et al., 2021 attrition is significantly higher for the Covid-19 group and attrition is associated with poorer pre-pandemic reading proficiency levels. In Kuhfeld et al. (2022) , between fall 2019 and fall 2021, the number of students testing in a grade dropped significantly more in high-poverty schools compared to their low-poverty counterparts. In other studies, which use non-representative samples including convenience samples (e.g., Moliner & Alegre, 2022 ), the direction of the bias is unclear. One exception is the paper by Meeter (2021) . In his sample the proportion of schools with a more disadvantaged student population appears to be slightly oversampled compared to all schools in the Netherlands, thus potentially biasing upwards the estimated impact of the pandemic on educational achievement. Finally, the question of how the use of non-appropriate methods to control for confounders might affect the estimated relationship between Covid-19 and student performance is addressed later when we discuss the results from the meta-regression analysis. As stated earlier, type of research design is one of our moderator variables.

Risk of bias domain: ROBINS-I.

2.6. Estimators and models

Two approaches frequently used in the meta-analysis literature are: 1) the Fixed Effects (FE) model, and 2) the Random Effects (RE) model. They rely on different assumptions. The FE model assumes that there is one true effect size common to all studies and that all differences in the observed effects can be attributed to within-study sampling error. By contrast, the RE model states that the effect size may vary between studies not only due to within-study sampling error, but also because there is heterogeneity in true effects between studies. Such additional variability is typically modelled employing a between-study variance parameter. Considering the characteristics of the studies included in our sample, it is difficult to assume that there is a common true effect that every study shares. Hence, it is anticipated that the RE model would be more suitable. Specifically, following the approach of Kaiser and Menkhoff (2020) , we estimate the mean of the distribution of true effects using a RE meta-analysis based on a Robust Variance Estimation (RVE). The RVE approach allows to account for the possibility that multiple effect sizes from the same study are not independent from each other. The benefits of this method are that there is no need to drop any effect size (to ensure their statistical independency) and no information is required about the intercorrelation between effect sizes within studies.

In an attempt to investigate factors driving heterogeneity among effect sizes, a meta-regression model is estimated:

where T i denotes the estimated Cohen's d effect size, Z i n is a vector of moderator variables, and ε i is the meta-regression disturbance term. The subscript i stands for the number of effect sizes included in the sample and the subscript n represents the number of moderator variables. In order to deal with the issue of heteroskedasticity in meta-regression analysis, we use Weighted Least Squares (WLS) with weights equal to the inverse of each estimate's standard error. This method is considered to be superior to widely employed RE estimators ( Stanley & Doucouliagos, 2013 ).

A relevant problem in estimating equation (1) lies in the identification of the moderator variables to be included in the model. Selecting incorrect variables leads to misspecification bias and invalid inference ( Xue et al., 2021 ). In line with several recent studies (e.g., Di Pietro, 2022 ; Gregor et al., 2021 ), the “general to specific” approach and the Bayesian Model Averaging (BMA) methodology are used to address model uncertainty. The advantages of the former method are that it addresses the issue of specification-searching bias and minimizes multicollinearity. Moderator variables are removed from the general specification in a stepwise fashion, dropping those with the largest p -value first until all the remaining variables are statistically significant. BMA is a method that runs many regressions containing different combinations of potential explanatory variables and weights them by model fit and complexity. Weighted averages of the estimated coefficients (posterior means) are computed using posterior model probabilities (akin to information criteria in frequentist econometrics). Each coefficient is also given a Posterior Inclusion Probability (PIP), which is the sum of posterior model probabilities of the models including the relevant variable and indicates how likely such a variable is to be contained in the true model ( Havránek et al., 2018 ).

2.7. Publication bias

Publication bias has long been identified as a major problem in meta-analysis ( Dwan et al., 2008 ). Such an issue occurs because editors and scholars tend to prefer publishing papers with statistically significant or non-controversial results. This may lead to distorted conclusions as published findings may end up overstating the true effect. Evidence of publication bias has been found in meta-analyses covering different fields (see, for instance, Begg and Belin (1988) in the case of medical studies).

In line with previous studies (e.g., Di Pietro, 2022 ), we use the Doi plot to graphically evaluate publication bias. Not only does the Doi plot enhance visualization of the asymmetry (in absence of publication bias there is no asymmetry), but it also allows for measuring the asymmetry through the Luis-Furuya-Kanamori (LFK) index. LFK index values within ±1 suggest no asymmetry, LFK index values exceeding ±1 but within ±2 indicate minor asymmetry, while LFK index values exceeding ±2 denote major asymmetry ( Furuya-Kanamori et al., 2018 ). As shown in Fig. 2 , the Doi plot shows no asymmetry (LFK index = 0), indicating that no publication bias is detected.

To further examine the risk of publication bias, we employ the Egger's test ( Egger et al., 1997 ) where the effect size is regressed against its precision (indexed by its standard error). Results indicate that we can safely accept the null hypothesis of no publication bias ( p -value = 0.380).

Our findings are consistent with those in previous relevant meta-analyses. König and Frey (2022) as well as Betthäuser et al. (2023) conclude that the presence of publication bias is unlikely.

3. Results and discussion

This Section is divided into three parts: first, we estimate a summary effect size (Section 3.1 .); second, we investigate potential sources of heterogeneity (Section 3.2 .); and third we provide a discussion of the main results (Section 3.3 .).

3.1. Summary effect size

In order to calculate the overall summary effect, we fit an intercept-only RE RVE model to our set of effect sizes. In such a model, the intercept can be interpreted as the precision-weighted mean effect size adjusted for effect-size dependence ( Friese et al., 2017 ).

The RVE RE mean effect size turns out to be −0.186 13 (SE = 0.0646, p -value = 0.0065, 95% CI [-0.316, −0.055]). It is also important to note that in this model the small-sample corrected degrees of freedom is greater than 4 (i.e., 39), suggesting that the p -value for the associated t -test accurately reflects the type I error ( Tanner-Smith et al., 2016 ).

Next, we compute the I 2 statistic to assess the heterogeneity of the results across studies ( Higgins et al., 2003 ). The appropriateness of the RE model is confirmed as I 2 has a value of 100%. 14 This suggests that all the variability in the effect-size estimates is due to heterogeneity as opposed to sampling error. Additionally, we also look at τ 2 (between-study variance), 15 which denotes the variability in the underlying true effects. Its large value of 1.74 further corroborates the hypothesis of substantial heterogeneity of the effect sizes ( Takase & Yoshida, 2021 ).

One should observe that our findings from the RVE analysis are broadly consistent with those from previous meta-analyses. Storey and Zhang (2021a) concluded that due to Covid-19 students lost, on average, 0.15 standard deviations of learning, König and Frey (2022) found average losses of 0.175 standard deviations, and Betthäuser et al. (2023) estimated average losses at 0.14 standard deviations. 16 Two considerations help put these results into perspective. First, one may notice that the delayed learning suffered by students as a result of Covid-19 school closure is roughly comparable to that experienced by their peers after major natural disasters. For instance, Sacerdote (2012) found that in the spring of 2006 students who were displaced by Katrina and Rita hurricanes saw their test scores fall by between 0.07 and 0.2 standard deviations. A similar result, though of a smaller magnitude, is obtained by Thamtanajit (2020) . He showed that in Thailand floods reduced student test scores by between 0.03 and 0.11 standard deviations, depending on the subject and educational level. Second, following Hanushek and Woessmann (2020) , a learning deficit of about 0.186 standard deviations can be considered to be equivalent to the loss of just over half of a school year. 17

While our results suggest that the pandemic lowered student performance on average by about 0.19 standard deviations, there is a large consensus that it did not affect students equally. For instance, several studies (see, for example, Engzell et al., 2021 ; Hevia et al., 2022 ) showed that Covid-19 had a detrimental effect especially on the achievement of students from less advantaged backgrounds. During school closures, these students are less likely to have had access to a computer, an internet connection, and a space conducive to learning ( Blaskó et al., 2022 ; Di Pietro et al., 2020 ). Moreover, as argued by Ariyo et al. (2022) , one would expect children of less educated parents to have received less parental support while learning at home than children of more educated parents. Greenlee and Reid (2020) provide evidence on this, showing that in Canada during the pandemic the frequency of children's participation in academic activities increased with parental educational levels.

3.2. Heterogeneity

Table 4 shows the results of regressing our standardised measure of student achievement against the moderator variables described above. Column (1) of Table 4 presents estimates from a regression where all potential explanatory variables are included. However, including all 13 variables (in addition to the constant term) in the regression may inflate standard errors and lead to inefficient estimates given that some of the variables may turn out to be redundant. Therefore, the “general-to-specific” approach is employed in an attempt to identify the influential factors. Following this strategy, as shown in Column (2) of Tables 4 , 6 independent variables (in addition to the constant term) are included in the model. To account for the potential dependence of multiple estimates reported by a given study, in Column (3) of Table 4 standard errors are clustered at the study level. Furthermore, since there are relatively few clusters (i.e., 39), following Cameron and Miller (2015) we apply the correction for small number of clusters by employing wild score bootstrapping ( Kline & Santos, 2012 ). Estimates shown in Column (3) indicate that a few moderator variables are robustly important. In line with expectations, students experienced larger learning deficits in math/science. More precisely, other things being equal, student achievement in math/science is on average found to be 0.17 standard deviations smaller than in humanities/subject mix. Our findings indicate also that the negative effect of Covid-19 on student achievement appears to be more pronounced when using experimental/quasi experimental techniques than when using descriptive or correlational research designs. Additionally, studies employing cross-sectional data as well as those focusing on non-European countries tend to suggest greater learning deficits.

Meta-regression results.

Note. Standard errors are in parentheses. Standard errors are clustered at study level (39 clusters) in Columns (3) and (4). In square brackets we report score wild cluster bootstrap p -values ( Kline & Santos, 2012 ) generated using boottest command in Stata with 999 replications ( Roodman, 2016 ). In Columns (1), (2), and (3) the regressions are estimated by weighted least squares where each effect size estimate is weighted by its inverse standard error. In Column (4), the regression is estimated by weighted least squares where each effect size estimate is weighted by its inverse variance.

*, **, and *** denote statistical significance at 10, 5, and 1%, respectively.

As a robustness test, the model depicted in Column (3) of Table 4 is re-estimated but this time each effect size is weighted by its inverse variance. As shown in Column (4) of Table 4 , with the exception of the estimate on longitudinal data, the sign and the magnitude of the other coefficients are broadly in line with those depicted in Column (3).

Next, the BMA approach is employed as an alternative to address the problem of uncertainty in the specification of the meta-regression model. 18 In BMA, following the rule of thumb proposed by Kass and Raftery (1995) , the significance of each explanatory factor is considered not to be weak if the PIP is larger than 0.5. The results, which are reported in Table 5 , show that all the variables that are consistently identified by the BMA methodology as relevant (i.e., Math/Science , Europe and Journal article ) are also included in the specification whose estimates are reported in Columns (2), (3) and (4) of Table 4 . Although the PIP associated with Quasi experimental / experimental does not quite make the relevant threshold, it is relatively close to it.

Bayesian model averaging (BMA).

3.3. Discussion of the main results

Our meta-analysis delivers six main results.

First, we find that, on average, the pandemic depressed student achievement by around 0.19 standard deviations. While this result is in line with the conclusions of earlier meta-analyses and systematic reviews, it should be taken into account that we use a more balanced sample in terms of country composition. This would suggest that our finding is more generalizable than that of previous studies.

Second, the pandemic caused a larger learning deficit in math/science compared to other subjects. This means that extra-support in math/science may be especially needed to help students catch up following the disruption caused by Covid-19.

Third, the effect of Covid-19 on student achievement does not appear to statistically differ across levels of education. Consistent with the findings of Betthäuser et al. (2023) , our results suggest that pandemic-related learning delays are similar across primary and secondary school students. In addition, this research has shown that these learning delays are not statistically different from the learning deficits suffered by tertiary education students. While, as discussed in Subsection 2.3.2 , one would have expected Covid-19 school closures to have had a more negative impact on the achievement of younger students than older students, this effect could have been offset by the greater support in terms of parental involvement received by the former group of students during online learning. Bubb and Jones (2020) found that in Norway, during the peak of the Covid-19 lockdown period, the proportion of parents/carers who reported having gained more information about their children's learning was higher in lower grades than in higher grades. Besides learners' age considerations, one should also observe that the shift towards online learning could have had a detrimental impact on the knowledge and skills of those students, mainly at secondary and tertiary levels, whose curriculum includes experiential learning experiences (e.g., field trips, hands-on activities) that cannot take place virtually ( Tang, 2022 ). However, at the same time, given that our analysis was not conducted at grade level, one cannot rule out the possibility that the pandemic has disproportionately affected the achievement of very young pupils (e.g., grade 1). In other words, there could be heterogeneity within primary school children.

Fourth, our results indicate that in 2021 students were not able to recover from the learning deficits caused by Covid-19 school closures in 2020. There is no statistically significant difference in student performance between assessments that have taken place several months or more than one year after the outbreak of the coronavirus and those that have occurred in the early stages of the pandemic. A similar finding has been obtained by Betthäuser et al. (2023) . It is important to note that, if not addressed, the learning deficits suffered by students may result in significant long-term consequences. Without remedial education upon school re-opening, not only may students who have been disproportionately affected by the pandemic continue to fall behind, but their learning achievements may also suffer a further setback as time goes on ( Angrist et al., 2021 ). Kaffenberger (2021) estimates that if learning in grade 3 is reduced by one-third, the equivalent of about a three-month school closure, learning levels in grade 10 would be a full year lower. Özdemir et al. (2022) forecast that the pandemic could erase decades-long gains in adult skills for affected cohorts unless interventions to alleviate learning deficits are quickly implemented. Additionally, several papers show that there is a relationship between test scores and labour market performance. For instance, Chetty et al. (2014) find that raising student achievement by 0.2 standard deviations is expected, on average, to increase annual lifetime earnings by 2.6%.

Fifth, the extent of the learning deficit seems to be smaller among students in Europe relative to their peers in the rest of the world. Although the reasons behind such a result are unclear, this might be due to several factors. First, one should note that the European countries considered in this study have, on average, a higher gross domestic product per capita than most of the non-European countries included in the analysis (this is not true for the US and Australia). As suggested by Donnelly and Patrinos (2020) , high-income countries are likely to have experienced smaller learning deficits as a result of Covid-19 because of their higher technological capability and the lower share of households living below the poverty line. 19 Second, Schleicher (2020) observes that the impact of the virus on education might have been less severe in many European countries and Southern Hemisphere countries whose 2019–2020 academic calendars had scheduled breaks (up to two weeks) that fell within the school closure period due to Covid-19. Third, there is evidence, but only available at higher education level, that European educational institutions were better prepared to respond to the challenges posed by the pandemic than their counterparts in other parts of the world. A survey carried out by the International Association of Universities immediately after the outbreak of the coronavirus shows that the percentage of higher education institutions where classroom teaching was replaced by distance teaching and learning was higher in Europe than in other continents ( Marinoni et al., 2020 ).

Sixth, our findings seem to suggest that studies using non-causal methods tend to underestimate the negative effect exerted by Covid-19 on student performance. The study by Betthäuser et al. (2023) also hints at the same conclusion, but their meta-analysis does not provide any evidence on this. As pointed out by Engzell et al. (2021) , non-causal methods fail to account for trends in student progress prior to the outbreak of Covid-19 and, hence, by assuming a counterfactual where achievement has stayed flat, they generate estimates of learning deficits that are biased downwards. The underestimation of pandemic-related learning delays may have important policy implications as it could result in under-provision of remedial support to students who are falling behind due to Covid-19.

4. Conclusions

We have assembled and studied a new sample of estimates about the impact of Covid-19 on student achievement. The sample includes 239 estimates from 39 studies covering 19 countries. One of the key findings emerging from our study is that the detrimental effects of Covid-19 school closure on student learning appear to be long-lasting. This calls for more efforts to help students recover from missed learning during the pandemic. As initiatives and programs aimed at learning recovery can be quite costly, several researchers (e.g., Patrinos, 2022 ) stress the importance of protecting the education budget whilst considering the competing financial needs of other sectors such as, for instance, health and social welfare ( UNESCO, 2020b ). Therefore, given the current policy climate where public resources are in high demand by various sectors, it is more important than ever to identify and adopt cost-effective measures.

While there seems to be a relatively large consensus in the literature that small group tutoring programs are a cost-effective way to mitigate the learning deficits caused by the pandemic (see, for instance, Burgess, 2020 ; Gortazar et al., 2022 ), less attention has been paid to a number of time- and cost-effective pedagogical practices ( Carrasco et al., 2021 ). Promoting the development of metacognition skills is, for instance, a powerful way to enhance student learning and performance ( Stanton et al., 2021 ). Metacognition allows students to think about their own learning, and this may increase their self-confidence and motivation. Similarly, increased collaboration and dialogue between students can support learning. Peers may help students clarify study materials and develop critical thinking. Overall, a better understanding is needed about the different types of educational interventions available and their cost-effectiveness. It would be desirable if governments at national, regional and local levels could exchange their experiences in this field and learn from each other.

Funding details

This work has not been supported by any grants.

CRediT authorship contribution statement

Giorgio Di Pietro: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing – review & editing.

Declaration of competing interest

No potential conflict of interest was reported by the author.

☆ The author would like to thank four anonymous referees for their helpful and constructive comments. The usual disclaimer applies.

2 In this study, the term “learning deficit” refers to the lower learning outcomes achieved by students due to the pandemic relative to what would have been expected if the pandemic had not occurred.

3 Previous meta-analyses include König and Frey (2022) who extracted 109 effect sizes nested in 18 studies, Storey and Zhang (2021a) who synthetized 79 effect sizes from 10 studies, and Betthäuser et al. (2023) who considered 291 effect sizes from 42 studies. The reviews by Patrinos et al. (2022) , Moscoviz and Evans (2022) , Donnelly and Patrinos (2022) , Hammerstein et al. (2021) and Zierer (2021) summarised the results of 35 studies, 29 studies, 8 studies, 11 studies and 9 studies, respectively.

4 Similarly, in Storey and Zhang (2021a) 7 out of the 10 studies considered in the meta-analysis are from the US or the UK.

5 One should, however, bear in mind that studies from high-income countries are strongly over-represented.

6 For Google Scholar, in line with the approach of Romanelli et al. (2021) , only the first 100 relevant references at each search were retrieved, as results beyond the first 100 entries were largely irrelevant given the purpose of this study.

7 These studies typically report in a table the mean test scores of the Covid-19 and non-Covid-19 cohorts, together with their corresponding standard deviations and information about the respective sample sizes of the two cohorts. Mean test scores ( X 1 , X 2 ) and their standard deviations ( S 1 , S 2 ) can be used to compute the Cohen's d (i.e., ( X 2 − X 1 ) ( S 1 2 + S 2 2 ) 2 ). Next, Cohen's d standard error can be computed using the formula given in Cooper and Hedges (1994) where information about the sample sizes of the two cohorts and the estimated Cohen's d are used.

8 For instance, in our sample, the journal article by Maldonado and De Witte was available online in 2021 but was published in 2022. On the other hand, the journal article by Ardington et al. was available online and published in 2021.

9 In this table, we report the actual year of publication of the latest version of the study (for journal articles this is the year when they are assigned a volume and issue number) rather than the year of the first appearance of a draft of the study in Google Scholar.

10 All the extracted effect sizes and their standard errors can be found in the supplementary Appendix.

11 One of the studies included in our sample (i.e., Kofoed et al., 2021 ) does use a randomized design.

12 Following Betthäuser et al. (2023) , the domain “deviation from intended interventions” was not considered. As noted by Hammerstein et al. (2021) , information on this domain is very rarely included in the relevant studies because Covid-19 school closures were not intended interventions.

13 The robumeta command in Stata is employed. An intercept-only model is run where the estimate of the meta regression constant is equal to the unconditional mean effect size across studies. With this command, it is possible to specify a value for rho , the expected correlation among dependent effects. Following Tanner-Smith and Tipton (2013) , we use different values of rho ranging from 0 to 1 in intervals of 0.1 in an attempt to check the consistency of results. All models yield the same outcome regardless of the specified value of rho .

14 A value of I 2 greater than 75% is considered large heterogeneity ( Higgins et al., 2003 ).

15 This is calculated using the method-of-moments estimator provided in Hedges et al. (2010) .

16 Relevant systematic reviews have also found similar learning deficits. Donnelly and Patrinos (2022) found average delays of 0.13 standard deviations, Zierer (2021) estimated average losses at 0.14 standard deviations, and Hammerstein et al. (2021) reported average deficits of 0.10 standard deviations.

17 They found that the loss of one third of a school year of learning is equivalent to approximately 11% of a standard deviation of lost test results. This finding is broadly consistent with that obtained by Hill et al. (2008) who conclude that a value of Cohen's d of 0.4 (with a margin of error of ±0.06) corresponds to the average annual reading achievement gains in fourth grade.

18 We treat all moderator variables as auxiliary covariates while the constant is treated as a focus regressor. Each effect size is weighted by its inverse standard error.

19 Results from the meta-analysis by Betthäuser et al. (2023) support this proposition.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.edurev.2023.100530 .

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Data availability

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• Published: 30 January 2023

A systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic

• Bastian A. Betthäuser   ORCID: orcid.org/0000-0002-4544-4073 1 , 2 , 3 ,
• Anders M. Bach-Mortensen   ORCID: orcid.org/0000-0001-7804-7958 2 &
• Per Engzell   ORCID: orcid.org/0000-0002-2404-6308 3 , 4 , 5  

Nature Human Behaviour volume  7 ,  pages 375–385 ( 2023 ) Cite this article

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To what extent has the learning progress of school-aged children slowed down during the COVID-19 pandemic? A growing number of studies address this question, but findings vary depending on context. Here we conduct a pre-registered systematic review, quality appraisal and meta-analysis of 42 studies across 15 countries to assess the magnitude of learning deficits during the pandemic. We find a substantial overall learning deficit (Cohen’s d  = −0.14, 95% confidence interval −0.17 to −0.10), which arose early in the pandemic and persists over time. Learning deficits are particularly large among children from low socio-economic backgrounds. They are also larger in maths than in reading and in middle-income countries relative to high-income countries. There is a lack of evidence on learning progress during the pandemic in low-income countries. Future research should address this evidence gap and avoid the common risks of bias that we identify.

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The coronavirus disease 2019 (COVID-19) pandemic has led to one of the largest disruptions to learning in history. To a large extent, this is due to school closures, which are estimated to have affected 95% of the world’s student population 1 . But even when face-to-face teaching resumed, instruction has often been compromised by hybrid teaching, and by children or teachers having to quarantine and miss classes. The effect of limited face-to-face instruction is compounded by the pandemic’s consequences for children’s out-of-school learning environment, as well as their mental and physical health. Lockdowns have restricted children’s movement and their ability to play, meet other children and engage in extra-curricular activities. Children’s wellbeing and family relationships have also suffered due to economic uncertainties and conflicting demands of work, care and learning. These negative consequences can be expected to be most pronounced for children from low socio-economic family backgrounds, exacerbating pre-existing educational inequalities.

It is critical to understand the extent to which learning progress has changed since the onset of the COVID-19 pandemic. We use the term ‘learning deficit’ to encompass both a delay in expected learning progress, as well as a loss of skills and knowledge already gained. The COVID-19 learning deficit is likely to affect children’s life chances through their education and labour market prospects. At the societal level, it can have important implications for growth, prosperity and social cohesion. As policy-makers across the world are seeking to limit further learning deficits and to devise policies to recover learning deficits that have already been incurred, assessing the current state of learning is crucial. A careful assessment of the COVID-19 learning deficit is also necessary to weigh the true costs and benefits of school closures.

A number of narrative reviews have sought to summarize the emerging research on COVID-19 and learning, mostly focusing on learning progress relatively early in the pandemic 2 , 3 , 4 , 5 , 6 . Moreover, two reviews harmonized and synthesized existing estimates of learning deficits during the pandemic 7 , 8 . In line with the narrative reviews, these two reviews find a substantial reduction in learning progress during the pandemic. However, this finding is based on a relatively small number of studies (18 and 10 studies, respectively). The limited evidence that was available at the time these reviews were conducted also precluded them from meta-analysing variation in the magnitude of learning deficits over time and across subjects, different groups of students or country contexts.

In this Article, we conduct a systematic review and meta-analysis of the evidence on COVID-19 learning deficits 2.5 years into the pandemic. Our primary pre-registered research question was ‘What is the effect of the COVID-19 pandemic on learning progress amongst school-age children?’, and we address this question using evidence from studies examining changes in learning outcomes during the pandemic. Our second pre-registered research aim was ‘To examine whether the effect of the COVID-19 pandemic on learning differs across different social background groups, age groups, boys and girls, learning areas or subjects, national contexts’.

We contribute to the existing research in two ways. First, we describe and appraise the up-to-date body of evidence, including its geographic reach and quality. More specifically, we ask the following questions: (1) what is the state of the evidence, in terms of the available peer-reviewed research and grey literature, on learning progress of school-aged children during the COVID-19 pandemic?, (2) which countries are represented in the available evidence? and (3) what is the quality of the existing evidence?

Our second contribution is to harmonize, synthesize and meta-analyse the existing evidence, with special attention to variation across different subpopulations and country contexts. On the basis of the identified studies, we ask (4) to what extent has the learning progress of school-aged children changed since the onset of the pandemic?, (5) how has the magnitude of the learning deficit (if any) evolved since the beginning of the pandemic?, (6) to what extent has the pandemic reinforced inequalities between children from different socio-economic backgrounds?, (7) are there differences in the magnitude of learning deficits between subject domains (maths and reading) and between age groups (primary and secondary students)? and (8) to what extent does the magnitude of learning deficits vary across national contexts?

Below, we report our answers to each of these questions in turn. The questions correspond to the analysis plan set out in our pre-registered protocol ( https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021249944 ), but we have adjusted the order and wording to aid readability. We had planned to examine gender differences in learning progress during the pandemic, but found there to be insufficient evidence to conduct this subgroup analysis, as the large majority of the identified studies do not provide evidence on learning deficits separately by gender. We also planned to examine how the magnitude of learning deficits differs across groups of students with varying exposures to school closures. This was not possible as the available data on school closures lack sufficient depth with respect to variation of school closures within countries, across grade levels and with respect to different modes of instruction, to meaningfully examine this association.

The state of the evidence

Our systematic review identified 42 studies on learning progress during the COVID-19 pandemic that met our inclusion criteria. To be included in our systematic review and meta-analysis, studies had to use a measure of learning that can be standardized (using Cohen’s d ) and base their estimates on empirical data collected since the onset of the COVID-19 pandemic (rather than making projections based on pre-COVID-19 data). As shown in Fig. 1 , the initial literature search resulted in 5,153 hits after removal of duplicates. All studies were double screened by the first two authors. The formal database search process identified 15 eligible studies. We also hand searched relevant preprint repositories and policy databases. Further, to ensure that our study selection was as up to date as possible, we conducted two full forward and backward citation searches of all included studies on 15 February 2022, and on 8 August 2022. The citation and preprint hand searches allowed us to identify 27 additional eligible studies, resulting in a total of 42 studies. Most of these studies were published after the initial database search, which illustrates that the body of evidence continues to expand. Most studies provide multiple estimates of COVID-19 learning deficits, separately for maths and reading and for different school grades. The number of estimates ( n  = 291) is therefore larger than the number of included studies ( n  = 42).

Flow diagram of the study identification and selection process, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

The geographic reach of evidence is limited

Table 1 presents all included studies and estimates of COVID-19 learning deficits (in brackets), grouped by the 15 countries represented: Australia, Belgium, Brazil, Colombia, Denmark, Germany, Italy, Mexico, the Netherlands, South Africa, Spain, Sweden, Switzerland, the UK and the United States. About half of the estimates ( n  = 149) are from the United States, 58 are from the UK, a further 70 are from other European countries and the remaining 14 estimates are from Australia, Brazil, Colombia, Mexico and South Africa. As this list shows, there is a strong over-representation of studies from high-income countries, a dearth of studies from middle-income countries and no studies from low-income countries. This skewed representation should be kept in mind when interpreting our synthesis of the existing evidence on COVID-19 learning deficits.

The quality of evidence is mixed

We assessed the quality of the evidence using an adapted version of the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool 9 . More specifically, we analysed the risk of bias of each estimate from confounding, sample selection, classification of treatments, missing data, the measurement of outcomes and the selection of reported results. A.M.B.-M. and B.A.B. performed the risk-of-bias assessments, which were independently checked by the respective other author. We then assigned each study an overall risk-of-bias rating (low, moderate, serious or critical) based on the estimate and domain with the highest risk of bias.

Figure 2a shows the distribution of all studies of COVID-19 learning deficits according to their risk-of-bias rating separately for each domain (top six rows), as well as the distribution of studies according to their overall risk of bias rating (bottom row). The overall risk of bias was considered ‘low’ for 15% of studies, ‘moderate’ for 30% of studies, ‘serious’ for 25% of studies and ‘critical’ for 30% of studies.

a , Domain-specific and overall distribution of studies of COVID-19 learning deficits by risk of bias rating using ROBINS-I, including studies rated to be at critical risk of bias ( n  = 19 out of a total of n  = 61 studies shown in this figure). In line with ROBINS-I guidance, studies rated to be at critical risk of bias were excluded from all analyses and other figures in this article and in the Supplementary Information (including b ). b , z curve: distribution of the z scores of all estimates included in the meta-analysis ( n  = 291) to test for publication bias. The dotted line indicates z  = 1.96 ( P  = 0.050), the conventional threshold for statistical significance. The overlaid curve shows a normal distribution. The absence of a spike in the distribution of the z scores just above the threshold for statistical significance and the absence of a slump just below it indicate the absence of evidence for publication bias.

In line with ROBINS-I guidance, we excluded studies rated to be at critical risk of bias ( n  = 19) from all of our analyses and figures, except for Fig. 2a , which visualizes the distribution of studies according to their risk of bias 9 . These are thus not part of the 42 studies included in our meta-analysis. Supplementary Table 2 provides an overview of these studies as well as the main potential sources of risk of bias. Moreover, in Supplementary Figs. 3 – 6 , we replicate all our results excluding studies deemed to be at serious risk of bias.

As shown in Fig. 2a , common sources of potential bias were confounding, sample selection and missing data. Studies rated at risk of confounding typically compared only two timepoints, without accounting for longer time trends in learning progress. The main causes of selection bias were the use of convenience samples and insufficient consideration of self-selection by schools or students. Several studies found evidence of selection bias, often with students from a low socio-economic background or schools in deprived areas being under-represented after (as compared with before) the pandemic, but this was not always adjusted for. Some studies also reported a higher amount of missing data post-pandemic, again generally without adjustment, and several studies did not report any information on missing data. For an overview of the risk-of-bias ratings for each domain of each study, see Supplementary Fig. 1 and Supplementary Tables 1 and 2 .

No evidence of publication bias

Publication bias can occur if authors self-censor to conform to theoretical expectations, or if journals favour statistically significant results. To mitigate this concern, we include not only published papers, but also preprints, working papers and policy reports.

Moreover, Fig. 2b tests for publication bias by showing the distribution of z -statistics for the effect size estimates of all identified studies. The dotted line indicates z  = 1.96 ( P  = 0.050), the conventional threshold for statistical significance. The overlaid curve shows a normal distribution. If there was publication bias, we would expect a spike just above the threshold, and a slump just below it. There is no indication of this. Moreover, we do not find a left-skewed distribution of P values (see P curve in Supplementary Fig. 2a ), or an association between estimates of learning deficits and their standard errors (see funnel plot in Supplementary Fig. 2b ) that would suggest publication bias. Publication bias thus does not appear to be a major concern.

Having assessed the quality of the existing evidence, we now present the substantive results of our meta-analysis, focusing on the magnitude of COVID-19 learning deficits and on the variation in learning deficits over time, across different groups of students, and across country contexts.

Learning progress slowed substantially during the pandemic

Figure 3 shows the effect sizes that we extracted from each study (averaged across grades and learning subject) as well as the pooled effect size (red diamond). Effects are expressed in standard deviations, using Cohen’s d . Estimates are pooled using inverse variance weights. The pooled effect size across all studies is d  = −0.14, t (41) = −7.30, two-tailed P  = 0.000, 95% confidence interval (CI) −0.17 to −0.10. Under normal circumstances, students generally improve their performance by around 0.4 standard deviations per school year 10 , 11 , 12 . Thus, the overall effect of d  = −0.14 suggests that students lost out on 0.14/0.4, or about 35%, of a school year’s worth of learning. On average, the learning progress of school-aged children has slowed substantially during the pandemic.

Effect sizes are expressed in standard deviations, using Cohen’s d , with 95% CI, and are sorted by magnitude.

Learning deficits arose early in the pandemic and persist

One may expect that children were able to recover learning that was lost early in the pandemic, after teachers and families had time to adjust to the new learning conditions and after structures for online learning and for recovering early learning deficits were set up. However, existing research on teacher strikes in Belgium 13 and Argentina 14 , shortened school years in Germany 15 and disruptions to education during World War II 16 suggests that learning deficits are difficult to compensate and tend to persist in the long run.

Figure 4 plots the magnitude of estimated learning deficits (on the vertical axis) by the date of measurement (on the horizontal axis). The colour of the circles reflects the relevant country, the size of the circles indicates the sample size for a given estimate and the line displays a linear trend. The figure suggests that learning deficits opened up early in the pandemic and have neither closed nor substantially widened since then. We find no evidence that the slope coefficient is different from zero ( β months  = −0.00, t (41) = −7.30, two-tailed P  = 0.097, 95% CI −0.01 to 0.00). This implies that efforts by children, parents, teachers and policy-makers to adjust to the changed circumstance have been successful in preventing further learning deficits but so far have been unable to reverse them. As shown in Supplementary Fig. 8 , the pattern of persistent learning deficits also emerges within each of the three countries for which we have a relatively large number of estimates at different timepoints: the United States, the UK and the Netherlands. However, it is important to note that estimates of learning deficits are based on distinct samples of students. Future research should continue to follow the learning progress of cohorts of students in different countries to reveal how learning deficits of these cohorts have developed and continue to develop since the onset of the pandemic.

The horizontal axis displays the date on which learning progress was measured. The vertical axis displays estimated learning deficits, expressed in standard deviation (s.d.) using Cohen’s d . The colour of the circles reflects the respective country, the size of the circles indicates the sample size for a given estimate and the line displays a linear trend with a 95% CI. The trend line is estimated as a linear regression using ordinary least squares, with standard errors clustered at the study level ( n  = 42 clusters). β months  = −0.00, t (41) = −7.30, two-tailed P  = 0.097, 95% CI −0.01 to 0.00.

Socio-economic inequality in education increased

Existing research on the development of learning gaps during summer vacations 17 , 18 , disruptions to schooling during the Ebola outbreak in Sierra Leone and Guinea 19 , and the 2005 earthquake in Pakistan 20 shows that the suspension of face-to-face teaching can increase educational inequality between children from different socio-economic backgrounds. Learning deficits during the COVID-19 pandemic are likely to have been particularly pronounced for children from low socio-economic backgrounds. These children have been more affected by school closures than children from more advantaged backgrounds 21 . Moreover, they are likely to be disadvantaged with respect to their access and ability to use digital learning technology, the quality of their home learning environment, the learning support they receive from teachers and parents, and their ability to study autonomously 22 , 23 , 24 .

Most studies we identify examine changes in socio-economic inequality during the pandemic, attesting to the importance of the issue. As studies use different measures of socio-economic background (for example, parental income, parental education, free school meal eligibility or neighbourhood disadvantage), pooling the estimates is not possible. Instead, we code all estimates according to whether they indicate a reduction, no change or an increase in learning inequality during the pandemic. Figure 5 displays this information. Estimates that indicate an increase in inequality are shown on the right, those that indicate a decrease on the left and those that suggest no change in the middle. Squares represent estimates of changes in inequality during the pandemic in reading performance, and circles represent estimates of changes in inequality in maths performance. The shading represents when in the pandemic educational inequality was measured, differentiating between the first, second and third year of the pandemic. Estimates are also arranged horizontally by grade level. A large majority of estimates indicate an increase in educational inequality between children from different socio-economic backgrounds. This holds for both maths and reading, across primary and secondary education, at each stage of the pandemic, and independently of how socio-economic background is measured.

Each circle/square refers to one estimate of over-time change in inequality in maths/reading performance ( n  = 211). Estimates that find a decrease/no change/increase in inequality are grouped on the left/middle/right. Within these categories, estimates are ordered horizontally by school grade. The shading indicates when in the pandemic a given measure was taken.

Learning deficits are larger in maths than in reading

Available research on summer learning deficits 17 , 25 , student absenteeism 26 , 27 and extreme weather events 28 suggests that learning progress in mathematics is more dependent on formal instruction than in reading. This might be due to parents being better equipped to help their children with reading, and children advancing their reading skills (but not their maths skills) when reading for enjoyment outside of school. Figure 6a shows that, similarly to earlier disruptions to learning, the estimated learning deficits during the COVID-19 pandemic are larger for maths than for reading (mean difference δ  = −0.07, t (41) = −4.02, two-tailed P  = 0.000, 95% CI −0.11 to −0.04). This difference is statistically significant and robust to dropping estimates from individual countries (Supplementary Fig. 9 ).

Each plot shows the distribution of COVID-19 learning deficit estimates for the respective subgroup, with the box marking the interquartile range and the white circle denoting the median. Whiskers mark upper and lower adjacent values: the furthest observation within 1.5 interquartile range of either side of the box. a , Learning subject (reading versus maths). Median: reading −0.09, maths −0.18. Interquartile range: reading −0.15 to −0.02, maths −0.23 to −0.09. b , Level of education (primary versus secondary). Median: primary −0.12, secondary −0.12. Interquartile range: primary −0.19 to −0.05, secondary −0.21 to −0.06. c , Country income level (high versus middle). Median: high −0.12, middle −0.37. Interquartile range: high −0.20 to −0.05, middle −0.65 to −0.30.

No evidence of variation across grade levels

One may expect learning deficits to be smaller for older than for younger children, as older children may be more autonomous in their learning and better able to cope with a sudden change in their learning environment. However, older students were subject to longer school closures in some countries, such as Denmark 29 , based partly on the assumption that they would be better able to learn from home. This may have offset any advantage that older children would otherwise have had in learning remotely.

Figure 6b shows the distribution of estimates of learning deficits for students at the primary and secondary level, respectively. Our analysis yields no evidence of variation in learning deficits across grade levels (mean difference δ  = −0.01, t (41) = −0.59, two-tailed P  = 0.556, 95% CI −0.06 to 0.03). Due to the limited number of available estimates of learning deficits, we cannot be certain about whether learning deficits differ between primary and secondary students or not.

Learning deficits are larger in poorer countries

Low- and middle-income countries were already struggling with a learning crisis before the pandemic. Despite large expansions of the proportion of children in school, children in low- and middle-income countries still perform poorly by international standards, and inequality in learning remains high 30 , 31 , 32 . The pandemic is likely to deepen this learning crisis and to undo past progress. Schools in low- and middle-income countries have not only been closed for longer, but have also had fewer resources to facilitate remote learning 33 , 34 . Moreover, the economic resources, availability of digital learning equipment and ability of children, parents, teachers and governments to support learning from home are likely to be lower in low- and middle-income countries 35 .

As discussed above, most evidence on COVID-19 learning deficits comes from high-income countries. We found no studies on low-income countries that met our inclusion criteria, and evidence from middle-income countries is limited to Brazil, Colombia, Mexico and South Africa. Figure 6c groups the estimates of COVID-19 learning deficits in these four middle-income countries together (on the right) and compares them with estimates from high-income countries (on the left). The learning deficit is appreciably larger in middle-income countries than in high-income countries (mean difference δ  = −0.29, t (41) = −2.78, two-tailed P  = 0.008, 95% CI −0.50 to −0.08). In fact, the three largest estimates of learning deficits in our sample are from middle-income countries (Fig. 3 ) 36 , 37 , 38 .

Two years since the COVID-19 pandemic, there is a growing number of studies examining the learning progress of school-aged children during the pandemic. This paper first systematically reviews the existing literature on learning progress of school-aged children during the pandemic and appraises its geographic reach and quality. Second, it harmonizes, synthesizes and meta-analyses the existing evidence to examine the extent to which learning progress has changed since the onset of the pandemic, and how it varies across different groups of students and across country contexts.

Our meta-analysis suggests that learning progress has slowed substantially during the COVID-19 pandemic. The pooled effect size of d  = −0.14, implies that students lost out on about 35% of a normal school year’s worth of learning. This confirms initial concerns that substantial learning deficits would arise during the pandemic 10 , 39 , 40 . But our results also suggest that fears of an accumulation of learning deficits as the pandemic continues have not materialized 41 , 42 . On average, learning deficits emerged early in the pandemic and have neither closed nor widened substantially. Future research should continue to follow the learning progress of cohorts of students in different countries to reveal how learning deficits of these cohorts have developed and continue to develop since the onset of the pandemic.

Most studies that we identify find that learning deficits have been largest for children from disadvantaged socio-economic backgrounds. This holds across different timepoints during the pandemic, countries, grade levels and learning subjects, and independently of how socio-economic background is measured. It suggests that the pandemic has exacerbated educational inequalities between children from different socio-economic backgrounds, which were already large before the pandemic 43 , 44 . Policy initiatives to compensate learning deficits need to prioritize support for children from low socio-economic backgrounds in order to allow them to recover the learning they lost during the pandemic.

There is a need for future research to assess how the COVID-19 pandemic has affected gender inequality in education. So far, there is very little evidence on this issue. The large majority of the studies that we identify do not examine learning deficits separately by gender.

Comparing estimates of learning deficits across subjects, we find that learning deficits tend to be larger in maths than in reading. As noted above, this may be due to the fact that parents and children have been in a better position to compensate school-based learning in reading by reading at home. Accordingly, there are grounds for policy initiatives to prioritize the compensation of learning deficits in maths and other science subjects.

A limitation of this study and the existing body of evidence on learning progress during the COVID-19 pandemic is that the existing studies primarily focus on high-income countries, while there is a dearth of evidence from low- and middle-income countries. This is particularly concerning because the small number of existing studies from middle-income countries suggest that learning deficits have been particularly severe in these countries. Learning deficits are likely to be even larger in low-income countries, considering that these countries already faced a learning crisis before the pandemic, generally implemented longer school closures, and were under-resourced and ill-equipped to facilitate remote learning 32 , 33 , 34 , 35 , 45 . It is critical that this evidence gap on low- and middle-income countries is addressed swiftly, and that the infrastructure to collect and share data on educational performance in middle- and low-income countries is strengthened. Collecting and making available these data is a key prerequisite for fully understanding how learning progress and related outcomes have changed since the onset of the pandemic 46 .

A further limitation is that about half of the studies that we identify are rated as having a serious or critical risk of bias. We seek to limit the risk of bias in our results by excluding all studies rated to be at critical risk of bias from all of our analyses. Moreover, in Supplementary Figs. 3 – 6 , we show that our results are robust to further excluding studies deemed to be at serious risk of bias. Future studies should minimize risk of bias in estimating learning deficits by employing research designs that appropriately account for common sources of bias. These include a lack of accounting for secular time trends, non-representative samples and imbalances between treatment and comparison groups.

The persistence of learning deficits two and a half years into the pandemic highlights the need for well-designed, well-resourced and decisive policy initiatives to recover learning deficits. Policy-makers, schools and families will need to identify and realize opportunities to complement and expand on regular school-based learning. Experimental evidence from low- and middle-income countries suggests that even relatively low-tech and low-cost learning interventions can have substantial, positive effects on students’ learning progress in the context of remote learning. For example, sending SMS messages with numeracy problems accompanied by short phone calls was found to lead to substantial learning gains in numeracy in Botswana 47 . Sending motivational text messages successfully limited learning losses in maths and Portuguese in Brazil 48 .

More evidence is needed to assess the effectiveness of other interventions for limiting or recovering learning deficits. Potential avenues include the use of the often extensive summer holidays to offer summer schools and learning camps, extending school days and school weeks, and organizing and scaling up tutoring programmes. Further potential lies in developing, advertising and providing access to learning apps, online learning platforms or educational TV programmes that are free at the point of use. Many countries have already begun investing substantial resources to capitalize on some of these opportunities. If these interventions prove effective, and if the momentum of existing policy efforts is maintained and expanded, the disruptions to learning during the pandemic may be a window of opportunity to improve the education afforded to children.

Eligibility criteria

We consider all types of primary research, including peer-reviewed publications, preprints, working papers and reports, for inclusion. To be eligible for inclusion, studies have to measure learning progress using test scores that can be standardized across studies using Cohen’s d . Moreover, studies have to be in English, Danish, Dutch, French, German, Norwegian, Spanish or Swedish.

Search strategy and study identification

We identified relevant studies using the following steps. First, we developed a Boolean search string defining the population (school-aged children), exposure (the COVID-19 pandemic) and outcomes of interest (learning progress). The full search string can be found in Section 1.1 of Supplementary Information . Second, we used this string to search the following academic databases: Coronavirus Research Database, the Education Resources Information Centre, International Bibliography of the Social Sciences, Politics Collection (PAIS index, policy file index, political science database and worldwide political science abstracts), Social Science Database, Sociology Collection (applied social science index and abstracts, sociological abstracts and sociology database), Cumulative Index to Nursing and Allied Health Literature, and Web of Science. Second, we hand-searched multiple preprint and working paper repositories (Social Science Research Network, Munich Personal RePEc Archive, IZA, National Bureau of Economic Research, OSF Preprints, PsyArXiv, SocArXiv and EdArXiv) and relevant policy websites, including the websites of the Organization for Economic Co-operation and Development, the United Nations, the World Bank and the Education Endowment Foundation. Third, we periodically posted our protocol via Twitter in order to crowdsource additional relevant studies not identified through the search. All titles and abstracts identified in our search were double-screened using the Rayyan online application 49 . Our initial search was conducted on 27 April 2021, and we conducted two forward and backward citation searches of all eligible studies identified in the above steps, on 14 February 2022, and on 8 August 2022, to ensure that our analysis includes recent relevant research.

Data extraction

From the studies that meet our inclusion criteria we extracted all estimates of learning deficits during the pandemic, separately for maths and reading and for different school grades. We also extracted the corresponding sample size, standard error, date(s) of measurement, author name(s) and country. Last, we recorded whether studies differentiate between children’s socio-economic background, which measure is used to this end and whether studies find an increase, decrease or no change in learning inequality. We contacted study authors if any of the above information was missing in the study. Data extraction was performed by B.A.B. and validated independently by A.M.B.-M., with discrepancies resolved through discussion and by conferring with P.E.

Measurement and standardizationr

We standardize all estimates of learning deficits during the pandemic using Cohen’s d , which expresses effect sizes in terms of standard deviations. Cohen’s d is calculated as the difference in the mean learning gain in a given subject (maths or reading) over two comparable periods before and after the onset of the pandemic, divided by the pooled standard deviation of learning progress in this subject:

Effect sizes expressed as β coefficients are converted to Cohen’s d :

We use a binary indicator for whether the study outcome is maths or reading. One study does not differentiate the outcome but includes a composite of maths and reading scores 50 .

Level of education

We distinguish between primary and secondary education. We first consulted the original studies for this information. Where this was not stated in a given study, students’ age was used in conjunction with information about education systems from external sources to determine the level of education 51 .

Country income level

We follow the World Bank’s classification of countries into four income groups: low, lower-middle, upper-middle and high income. Four countries in our sample are in the upper-middle-income group: Brazil, Colombia, Mexico and South Africa. All other countries are in the high-income group.

Data synthesis

We synthesize our data using three synthesis techniques. First, we generate a forest plot, based on all available estimates of learning progress during the pandemic. We pool estimates using a random-effects restricted maximum likelihood model and inverse variance weights to calculate an overall effect size (Fig. 3 ) 52 . Second, we code all estimates of changes in educational inequality between children from different socio-economic backgrounds during the pandemic, according to whether they indicate an increase, a decrease or no change in educational inequality. We visualize the resulting distribution using a harvest plot (Fig. 5 ) 53 . Third, given that the limited amount of available evidence precludes multivariate or causal analyses, we examine the bivariate association between COVID-19 learning deficits and the months in which learning was measured using a scatter plot (Fig. 4 ), and the bivariate association between COVID-19 learning deficits and subject, grade level and countries’ income level, using a series of violin plots (Fig. 6 ). The reported estimates, CIs and statistical significance tests of these bivariate associations are based on common-effects models with standard errors clustered by study, and two-sided tests. With respect to statistical tests reported, the data distribution was assumed to be normal, but this was not formally tested. The distribution of estimates of learning deficits is shown separately for the different moderator categories in Fig. 6 .

Pre-registration

We prospectively registered a protocol of our systematic review and meta-analysis in the International Prospective Register of Systematic Reviews (CRD42021249944) on 19 April 2021 ( https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021249944 ).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data used in the analyses for this manuscript were compiled by the authors based on the studies identified in the systematic review. The data are available on the Open Science Framework repository ( https://doi.org/10.17605/osf.io/u8gaz ). For our systematic review, we searched the following databases: Coronavirus Research Database ( https://proquest.libguides.com/covid19 ), Education Resources Information Centre database ( https://eric.ed.gov ), International Bibliography of the Social Sciences ( https://about.proquest.com/en/products-services/ibss-set-c/ ), Politics Collection ( https://about.proquest.com/en/products-services/ProQuest-Politics-Collection/ ), Social Science Database ( https://about.proquest.com/en/products-services/pq_social_science/ ), Sociology Collection ( https://about.proquest.com/en/products-services/ProQuest-Sociology-Collection/ ), Cumulative Index to Nursing and Allied Health Literature ( https://www.ebsco.com/products/research-databases/cinahl-database ) and Web of Science ( https://clarivate.com/webofsciencegroup/solutions/web-of-science/ ). We also searched the following preprint and working paper repositories: Social Science Research Network ( https://papers.ssrn.com/sol3/DisplayJournalBrowse.cfm ), Munich Personal RePEc Archive ( https://mpra.ub.uni-muenchen.de ), IZA ( https://www.iza.org/content/publications ), National Bureau of Economic Research ( https://www.nber.org/papers?page=1&perPage=50&sortBy=public_date ), OSF Preprints ( https://osf.io/preprints/ ), PsyArXiv ( https://psyarxiv.com ), SocArXiv ( https://osf.io/preprints/socarxiv ) and EdArXiv ( https://edarxiv.org ).

Code availability

All code needed to replicate our findings is available on the Open Science Framework repository ( https://doi.org/10.17605/osf.io/u8gaz ).

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Acknowledgements

Carlsberg Foundation grant CF19-0102 (A.M.B.-M.); Leverhulme Trust Large Centre Grant (P.E.), the Swedish Research Council for Health, Working Life and Welfare (FORTE) grant 2016-07099 (P.E.); the French National Research Agency (ANR) as part of the ‘Investissements d’Avenir’ programme LIEPP (ANR-11-LABX-0091 and ANR-11-IDEX-0005-02) and the Université Paris Cité IdEx (ANR-18-IDEX-0001) (P.E.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Impact of COVID-19 on Higher Education: Critical Reflections

• Original Article
• Published: 11 August 2022
• Volume 35 , pages 563–567, ( 2022 )

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• Ka Ho Mok   ORCID: orcid.org/0000-0003-0846-1867 1  

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This Special Issue has chosen the major focus to examine how the COVID-19 pandemic has affected higher education development and governance. The collection of articles in this Special Issue is organized with three key sub-themes, namely, student mobility, teaching and student learning, and university governance. Papers selected in this Issue were presented at different international conferences examining how the outbreak of the COVID-19 pandemic in late 2019 has affected higher education development from international and comparative perspectives. During the international research events, authors contributing their papers to this Special Issue indeed benefitted from the exchanges and dialogues from international peers. Drawing insights from the papers collected in this Special Issue, this introductory article concludes by drawing the implications for future development of international education.

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Teaching and Student Learning

The outbreak of the COVID-19 pandemic has pushed higher education systems across different parts of the globe to adopt online platforms for conducting teaching and learning activities. Angela Hou and colleagues , in her article, ask a very important and reflective question: How would COVID-19 drive digitalization, innovations, and crisis management of higher education? More importantly, they also raise the issue of quality assurance when most higher education teaching and learning had been operating through online platforms. Based upon a case study of the INQAAHE Virtual Review, they critically examine issues related to quality assurance when higher education teaching and learning of had been digitalized. Their article does not only offer a case study of Taiwan, showing how one of the East Asian economies responded to the outbreak of the COVID-19 crisis through digitalizing higher education. This case studies also shows relevance to other parts of the world, especially when those countries/regions encounter difficulties in realizing the digitalization of teaching and student learning. International research reports educational inequality and disparity being intensified after the widespread the COVID-19 pandemic (UNESCO, 2020 , 2021 ). International and comparative research report higher education systems from relatively low-income countries/regions have suffered tremendously simply because of the lack of resources/infrastructural support for online teaching/learning, let alone diverse differences in educational cultures/management and practices across different parts of the globe (Vegas, 2020 ; Mok, et al., 2021 ).

The second article contributed by Mok , Xiong, and Ke critically examines how Chinese students evaluate overseas studies during and in the post-COVID-19 crisis, showing the growing interest of Chinese students in making Asia their future destination for studying abroad, especially when becoming more concerned about public health conditions in traditional destinations based in Europe, the UK and the USA (QS, 2020 ). The motivations and desires of Chinese students choosing overseas learning would have been affected by the new geopolitics and different kinds of “cultural shocks”, particularly when Asian students were reportedly being discriminated/stigmatized after the outbreak of the COVID-19 pandemic when studying abroad (Mbous, et al2022; Mok and Zhang, 2021 ).

Institutional Response and University Governance

Moving beyond management of teaching and student learning, Susan Robertson, critically reflecting upon the future of higher education governance set against the COVID-19 context, presented a paper at the Conference for Higher Education Research (CHER) 2020. Based upon recent works on temporality and higher education, Robertson considers such works have made important contributions to work on time, though time-future continues to be under-developed. In her presentation, she attempted to explore anticipatory governance in the contemporary university. Exploring a range of anticipatory practices and their logic in the contemporary academy, from goals to planning, predictions, forecasts, indicators, specialised knowledge, and agreements, Robertson believed we should think beyond our own box of how the future presents potential opportunities for academic development. Adopting the time-future lens in conceiving future university governance, Robertson’s paper shows the anticipatory practices mobilise different kinds of socio-temporal and political sensibilities and expectations, practices, and institutional arrangements, that constitute timescapes in the contemporary academy (Robertson, 2020 ).

Whereas Robertson discusses temporality in general, Tilak critically examines the impact of the pandemic on Indian higher education. In his article, he presents the major challenges confronting higher education development in India against the COVID-19 crisis, discussing major strategies/policy measures adopted by the Indian government in managing challenges for higher education. As India is committed to further increasing its higher education enrolments in order to produce sufficient young talents for the changing economic needs of the country, the current COVID-19 crisis would considerably disrupt its plans for higher education development. To which extent the Indian government and university leaders make use of innovative measures through the technology-enabled platforms to achieve its development goals depends not only on resources but also on careful policy coordination.

Moving away from Asia, the article contributed by O’Shea, Mou, Xu, and Aikins critically examine how higher education institutions (HEIs) in three countries, namely, Canada, China, and the USA, responded to the challenges of COVID-19 over a six-month period at the outbreak of the global pandemic. Employing document analysis, they analyze 732 publicly available communications from 27 HEIs in Canada, China, and the United States. Through the theoretical framework of Situational Critical Communications Theory (SCCT), O’Shea et al ., explore how HEIs respond to the crisis and communicate their response to the crisis to campus stakeholders. While there are important country-level distinctions among HEIs in how they communicate and respond to crisis, this research finds there are common themes across the three countries, including (1) emphasizing social responsibilities of serving the community, (2) referencing public health guidelines, and (3) offering different kinds of financial support to students. The findings shed light on strengths and weaknesses of the SCCT model in analyzing HEI responses to COVID-19 and may be helpful for HEIs to prepare for the next crisis.

Future of International Education

After the outbreak of the COVID-19 pandemic, international students are considered to be more adversely affected by COVID-19 restrictions than other student and population groups (e.g., local students) in the world (Dodd, et al., 2021 ). According to research conducted by Amoah and Mok in 2020, international students find themselves living in foreign countries/regions with limited social and economic support and in a context of rising discrimination (Amoah and Mok, 2020 ). With special attention to international student well-being, the article contributed by Amoah and Esther Mok examines the effects that COVID-19 restrictions have had and are having on the lives of international students. Such effects include direct consequences of the disease itself and its disruptive effect on this group of students and the effectiveness of the support offered by universities for the well-being of international students. The study analyzed data from a global survey conducted among international students in April 2020. They found that the well-being of international students is negatively associated with being worried about COVID-19 itself ( B = − 0.218, p = .027); with perceived COVID-19 disruption of academic activities ( B = − 0.162, p = .016); and with feelings of loneliness ( B = − 0.317, p = .000). Notably, COVID-19 information support provided by universities was positively associated with the students’ well-being ( B = 0.224, p = 0.003). These findings are discussed in the context of education policy and practical changes introduced by the COVID-19 pandemic. The discussion also considers the influence of the changing geopolitical and social environment (e.g., racism) on higher education internationalisation, critically reflecting upon management and governance issues faced by universities worldwide when promoting the well-being of international students (Mok, 2022 ).

A critical reflection of how the COVID-19 pandemic has disrupted the Australian university system, Anthony Welch shows the impact of COVID as a stark reminder that international students are so much more than cash cows for universities. Not merely do they add immeasurably to the vibrant cultural diversity of universities, they “are vital parts of communities. Indeed, many international students are future Australian citizens. It is estimated that between 20,000 and 30,000 international students move from student visas to permanent residency visas every year” a figure that is likely to be an underestimate, since students often gain another form of temporary visa, before attaining permanent residence. During the COVID-19 crisis, we have witnessed how academic cooperation and research collaboration have become highly politicized, especially when the new geopolitics has emerged as an influential force shaping international education and research.

In view of the worsening diplomatic relationship between China and Australia, Welch highlights the potential for COVID to curtail staff and student mobility, restricting research collaboration between colleagues in Australia and China. The growing anti-Chinese and anti-Asian sentiments commonly found not only in Australia but also in other major university systems in Europe and North America would create disincentives for inter-university and cross-border collaboration, which would be detrimental to future development of international education and research. According to Welch, what is urgently needed is a dialogue of civilizations, rather than a clash of civilizations, with the associated rancorous and rivalrous international relations that threaten international academic mobility and collaboration.

This Special Issue brings together thought-provocative pieces, critically reflecting upon the impact of the COVID-19 pandemic on higher education development. The challenges confronting contemporary universities are partly caused by the pandemic, disrupting the “normal operation” of universities. Nonetheless, the present global health crisis has also opened new opportunities for university teachers and leaders for exploring innovative modes of teaching and student learning, moving beyond the conventional models in developing new forms of inter-university collaborations. However, part of the problems facing universities globally is the unfavorable influences of new geopolitics creating mistrust across countries/regions. Perhaps world leaders as well as university leaders should be humbled to learn from the global health crisis resulting from the outbreak of COVID-19, seeking appropriate ways for closer and deeper collaboration for the betterment of the humanity.

Amoah, P.A. and Mok, K.H. (2020) ‘The Covid-19 pandemic and internationalisation of higher education: International students knowledge, experiences and well-being’, Higher Education Policy Institute's blog , 13 June. Available on https://www.hepi.ac.uk/2020/06/13/weekend-reading-the-covid-19-pandemic-and-internationalisation-of-higher-education-international-students-knowledge-experiences-and-wellbeing/ , accessed 18 June 2020.

Dodd, R.H., Dadaczynski, K., Okan, O., McCaffery, K.J. and Pickles, K. (2021) Psychological Wellbeing and Academic Experience of University Students in Australia during COVID-19. International Journal of Environmental Research and Public Health 18 (3): 866

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Mok, K.H. (2022) 'COVID-19 Pandemic and International Higher Education Major Challenges and Implications for East Asia', in S. Marginson and X. Xu (eds.) Higher Education in East Asia Internationalization Strategy and National Agendas London: Bloomsbury, pp. 225–246.

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Mok, K.H. and Zhang, Y.L. (2021) ‘Remaking International Higher Education for an Unequal World’, Higher Education Quarterly . Published online 13 November. doi.org/ https://doi.org/10.1111/hequ.12366 .

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New UNESCO global survey reveals impact of COVID-19 on higher education

In the wake of the unprecedented COVID-19 education disruptions which affected more than 220 million tertiary-level students around the world, UNESCO conducted a global survey aimed at providing an evidence-based overview of the current situation of the higher education system at national and global levels.

The results provide insights on how some countries were able to transform challenges, brought by the rapid digitalization of education, into opportunities through strong government support and international cooperation.

The survey attempts to assess the varying impact the pandemic had on higher education systems in terms of access, equity and quality of teaching and learning, university operation, national challenges, emerging issues, and strategic responses.

 The key findings for the various assessment dimensions are:

 Mode of teaching and learning: The major impact of COVID-19 on teaching and learning is the increase in online education. The hybrid mode of teaching has become the most popular form. 

• Access : The impact of COVID-19 on enrollment varies by regional and income levels. High income and Europe and North American countries are better able to cope with the disruption due to government funding support and increase in domestic enrollment.
• International mobility : Mobility took a major hit, affecting international students significantly, but virtual mobility could compensate or even replace physical mobility. 
• University staff : Despite the closure of many universities, the impact of COVID-19 on university staff compared to the previous academic year is limited.  
• Disruption of research and extension activities : COVID-19 caused suspension and cancellation of teaching and research activities globally. 
• Widening inequality : The mixed impact of the pandemic on university finance shed a light on the exacerbation of inequality in higher education. Financial support from the government and external sources are crucial to the survival of HEIs. 
• University operations : The strong impact of the pandemic on HEIs operations caused reduced maintenance and services on campus and campuses closures worldwide.
• National challenges : Health and adaptation to new modes and models of teaching are the top concerns for students and institutions. 
• Transition from higher education to work : The significant reduction of job opportunities makes the transition from higher education to the labor market more difficult. Employers are also seeking applicants with higher technology skills. 
• National priority : Strategic options for country-specific response are to improve infrastructure and availability of digital devices for online or distance learning as well as support for teachers and more international collaboration in research and policy dialogues.

The global survey was addressed to the 193 UNESCO Member States and 11 Associate Members. Sixty-five countries submitted responses, fifty-seven of which were used for the analysis that informed the report.

• Access the full report
• More on UNESCO’s work in higher education

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Article Versions Notes

Fisher, Y.; Shatz-Oppenheimer, O.; Arviv Elyashiv, R. The Effect of COVID-19 on a Short-Term Teacher-Education Program: The Israeli Case. Educ. Sci. 2024 , 14 , 958. https://doi.org/10.3390/educsci14090958

Fisher Y, Shatz-Oppenheimer O, Arviv Elyashiv R. The Effect of COVID-19 on a Short-Term Teacher-Education Program: The Israeli Case. Education Sciences . 2024; 14(9):958. https://doi.org/10.3390/educsci14090958

Fisher, Yael, Orna Shatz-Oppenheimer, and Rinat Arviv Elyashiv. 2024. "The Effect of COVID-19 on a Short-Term Teacher-Education Program: The Israeli Case" Education Sciences 14, no. 9: 958. https://doi.org/10.3390/educsci14090958

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