essay on covid 19 and its impact on education

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

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

March 3, 2022

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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The Impact of the COVID-19 Pandemic on Education Learning

• First Online: 15 November 2023

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The COVID-19 pandemic has caused a global disruption in everyday life, including education, since March 2020. The closure of schools due to COVID-19 has resulted in a break in education provision that has produced long-lasting learning losses. Furthermore, school closures may increase educational inequality. Although online education can substitute for in-person learning, it is an imperfect replacement.

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Sanz, I., Tena, J.D. (2023). The Impact of the COVID-19 Pandemic on Education Learning. In: Sainz, J., Sanz, I. (eds) Addressing Inequities in Modern Educational Assessment. Springer, Cham. https://doi.org/10.1007/978-3-031-45802-6_2

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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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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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How COVID-19 caused a global learning crisis

Executive summary.

In our latest report on unfinished learning, we examine the impact of the COVID-19 pandemic on student learning and well-being, and identify potential considerations for school systems as they support students in recovery and beyond. Our key findings include the following:

• The length of school closures varied widely across the world. School buildings in middle-income Latin America and South Asia were fully or partially closed the longest—for 75 weeks or more. Those in high-income Europe and Central Asia were fully or partially closed for less time (30 weeks on average), as were those in low-income sub-Saharan Africa (34 weeks on average).

About the authors

This article is a collaborative effort by Jake Bryant , Felipe Child , Emma Dorn , Jose Espinosa, Stephen Hall , Topsy Kola-Oyeneyin , Cheryl Lim, Frédéric Panier, Jimmy Sarakatsannis , Dirk Schmautzer , Seckin Ungur , and Bart Woord, representing views from McKinsey’s Education Practice.

• Access to quality remote and hybrid learning also varied both across and within countries. In Tanzania, while school buildings were closed, children in just 6 percent of households listened to radio lessons, 5 percent accessed TV lessons, and fewer than 1 percent participated in online learning. 1 Jacobus Cilliers and Shardul Oza, “What did children do during school closures? Insights from a parent survey in Tanzania,” Research on Improving Systems of Education (RISE), May 19, 2021.
• Furthermore, pandemic-related learning delays stack up on top of historical learning inequities. The World Bank estimates that while students in high-income countries gained an average of 50 harmonized learning outcomes (HLO) points a year prepandemic, students in low-income countries were gaining just 20, leaving those students several years behind. 2 Noam Angrist et al., “Measuring human capital using global learning data,” Nature , March 2021, Volume 592.
• High-performing systems, with relatively high levels of pre-COVID-19 performance, where students may be about one to five months behind due to the pandemic (for example, North America and Europe, where students are, on average, four months behind).
• Low-income prepandemic-challenged systems, with very low levels of pre-COVID-19 learning, where students may be about three to eight months behind due to the pandemic (for example, sub-Saharan Africa, where students are on average six months behind).
• Pandemic-affected middle-income systems, with moderate levels of pre-COVID-19 learning, where students may be nine to 15 months behind (for example, Latin America and South Asia, where students are, on average, 12 months behind).
• The pandemic also increased inequalities within systems. For example, it widened gaps between majority Black and majority White schools in the United States and increased preexisting urban-rural divides in Ethiopia.
• Beyond learning, the pandemic has had broader social and emotional impacts on students globally—with rising mental-health concerns, reports of violence against children, rising obesity, increases in teenage pregnancy, and rising levels of chronic absenteeism and dropouts.
• Lower levels of learning translate into lower future earnings potential for students and lower economic productivity for nations. By 2040, the economic impact of pandemic-related learning delays could lead to annual losses of $1.6 trillion worldwide, or 0.9 percent of total global GDP.
• Resilience: Safely reopen schools for in-person learning while ensuring resilience for future disruptions.
• Reenrollment: Encourage students, families, and teachers to reengage with learning in effective learning environments.
• Recovery: Support students as they recover from the academic and social-emotional impacts of the pandemic, starting with an understanding of each student’s needs.
• Reimagining: Recommit to quality education for every child, doubling down on the fundamentals of educational excellence and innovating to adapt.

In some parts of the world, students, parents, and teachers may be experiencing a novel feeling: cautious optimism. After two years of disruptions from COVID-19, the overnight shift to online and hybrid learning, and efforts to safeguard teachers, administrators, and students, cities and countries are seeing the first signs of the next normal. Masks are coming off. Events are being held in person. Extracurricular activities are back in full swing.

These signs of hope are counterbalanced by the lingering, widespread impact of the pandemic. While it’s too early to catalog all of the ways students have been affected, we are starting to see initial indications of the toll COVID-19 has taken on learning around the world. Our analysis of available data found no country was untouched, but the impact varied across regions and within countries. Even in places with effective school systems and near-universal connectivity and device access, learning delays were significant, especially for historically vulnerable populations. 3 Emma Dorn, Bryan Hancock, Jimmy Sarakatsannis, and Ellen Viruleg, “ COVID-19 and education: An emerging K-shaped recovery ,” McKinsey, December 14, 2021. In many countries that had poor education outcomes before the pandemic, the setbacks were even greater. In those countries, an even more ambitious, coordinated effort will likely be required to address the disruption students have experienced.

Our analysis highlights the extent of the challenge and demonstrates how the impact of the pandemic on learning extends across students, families, and entire communities. Beyond the direct effect on students, learning delays have the potential to affect economic growth: by 2040, according to McKinsey analysis, COVID-19-related unfinished learning could translate into $1.6 trillion in annual losses to the global economy.

Acting decisively in the near term could help to address learning delays as well as the broader social, emotional, and mental-health impact on students. In mobilizing to respond to the pandemic’s effect on student learning and thriving, countries also may need to reassess their education systems—what has been working well and what may need to be reimagined in light of the past two years. Our hope is that this article’s analysis provides a potential starting point for dialogue as nations seek to reinvigorate their education systems.

Gauging the pandemic’s widespread impact on education

One of the challenges in assessing the global effect of the pandemic on learning is the lack of data. Comparative international assessments mostly cover middle- to high-income countries and have not been carried out since the beginning of the pandemic. The next Program for International Student Assessment (PISA), for example, was delayed until 2022. 4 “PISA,” OECD, accessed March 30, 2022. Similarly, many countries had to cancel or defer national assessments. As a result, few nations have a complete data set, and many have no assessment data to indicate relative learning before and since school closures. Accordingly, our methodology used available data augmented by informed assumptions to get a directional picture of the pandemic’s effects on the scholastic achievement and well-being of students.

The pandemic’s impact on student learning

We evaluated the potential effect of the pandemic on student learning by multiplying the amount of time school was disrupted in each country by the estimated effectiveness of the schooling students received during disruptions.

The duration of school closures ran the gamut. During the 102-week period we studied (from the onset of COVID-19 to January 2022), school buildings in Latin America, including the Caribbean, and South Asia were fully or partially closed for 75 weeks or more, while those in Europe and Central Asia were fully or partially closed for an average of 30 weeks (Exhibit 1). Schools in some regions began reopening a few months into the pandemic, but as of January 2022, more than a quarter of the world’s student population resided in school systems where schools were not yet fully open.

Remote and hybrid learning similarly varied widely across and within countries. Some students were supported by internet access, devices, learning management systems, adaptive learning software, live videoconferencing with teachers and peers, and home environments with parents or hired professionals to support remote learning. Others had access to radio or television programs, paper packages, and text messaging. Some students may not have had access to any learning options. 5 What’s next? Lessons on education recovery: Findings from a survey of ministries of education amid the COVID-19 pandemic , UNESCO, UNICEF, the World Bank, and OECD, June 2021. We used the World Bank’s estimates on “mitigation effectiveness” by country income level to account for different levels of access to learning tools and quality through the pandemic (see the forthcoming methodological appendix for more details).

Our model suggests that in the first 23 months since the start of the pandemic, students around the world may have lost about eight months of learning, on average, with meaningful disparities across and within regions and countries. For example, students in South Asia, Latin America, and the Caribbean may be more than a year behind where they would have been absent the pandemic. In North America and Europe, students might be an average of four months behind (Exhibit 2).

The regional numbers only begin to tell the full story. The greater the range of school system performance and resources across regions, the greater the variation in student experiences. Students in Japan and Australia may be less than two months behind, while students in the Philippines and Indonesia may be more than a year behind where they would have been (Exhibit 3).

Within countries, the impact of COVID-19 has also affected individual students differently. Wherever assessments have taken place since the onset of the pandemic, they suggest widening gaps in both opportunity and achievement. Historically vulnerable and marginalized students are at an increased risk of falling further behind.

In the United States, students in majority Black schools were half a year behind in mathematics and reading by fall 2021, while students in majority White schools were just two months behind. 6 “ COVID-19 and education: An emerging K-shaped recovery ,” December 14, 2021. In Ethiopia, students in rural areas achieved under one-third of the expected learning from March to October 2020, while those in urban areas learned less than half of the expected amount. 7 Research on Improving Systems of Education (RISE) , “Learning inequalities widen following COVID-19 school closures in Ethiopia,” blog entry by Janice Kim, Pauline Rose, Ricardo Sabates, Dawit Tibebu Tiruneh, and Tassew Woldehanna, May 4, 2021. Assessments in New South Wales, Australia, detected minimal impact on learning overall, but third-grade students in the most disadvantaged schools experienced two months less growth in mathematics. 8 Leanne Fray et al., “The impact of COVID-19 on student learning in New South Wales primary schools: An empirical study,” The Australian Educational Researcher , 2021, Volume 48.

Would you like to learn more about our Education Practice ?

Covid-19-related losses on top of historical inequalities.

The learning crisis is not new. In the years before COVID-19, many school systems faced challenges in providing learning opportunities for many of their students. The World Bank estimates that before the pandemic, more than half of students in low- and middle-income countries were living in “learning poverty”—unable to read and understand a simple text by age ten. That number may rise as high as 70 percent due to pandemic-related school disruptions. 9 Joao Azevedo et al., “The state of the global education crisis: A path to recovery,” World Bank Group, December 3, 2021.

The World Bank’s harmonized learning outcomes (HLOs) compare learning achievement and growth across countries. This measure combines multiple global student assessments into one metric, with a range of 625 for advanced attainment and 300 for minimum attainment. According to the World Bank’s 2018 HLO database, students from some countries in the Middle East, North Africa, and South Asia were several years behind their counterparts in North America and Europe before the pandemic (Exhibit 4). 10 Data Blog , “Harmonized learning outcomes: transforming learning assessment data into national education policy reforms,” blog entry by Harry A. Patrinos and Noam Angrist, August 12, 2019.

Students in these countries were also progressing more slowly each year in school. While students in high-income countries may have been gaining 50 HLO points in a year, students in low-income countries were gaining just 20. In other words, not much learning was happening in some countries even before the pandemic.

Prepandemic learning levels and pandemic-related learning delays interacted in different ways in different countries and regions. Although each country is unique, three archetypes emerge based on the performance of education systems (Exhibit 5).

High-performing systems. Countries in this archetype generally had higher pre-COVID-19 learning levels. Systems had more capacity for remote learning, and school buildings remained closed for shorter time periods. 11 “Education: From disruption to recovery,” UNESCO, accessed March 11, 2022. Data suggest that after the initial shock of the pandemic in 2020, learning delays increased only moderately with subsequent school closures in the 2021–22 school year. Some high-income countries seem to show little evidence of decreased learning overall. According to the Australian National Assessment Program–Literacy and Numeracy (NAPLAN), the COVID-19 pandemic did not have a statistically significant impact on average student literacy and numeracy levels, even in Victoria, where learning was remote for more than 120 days. 12 “Highlights from Victorian preliminary results in NAPLAN 2021,” Victoria state government, August 26, 2021; Adam Carey, Melissa Cunningham, and Anna Prytz, “‘Children have suffered enormously’: School closures leave experts divided,” The Age , Melbourne, July 25, 2021. However, in many high-income countries, the impact of the pandemic on learning remained significant. Assessments of student learning in the United States in fall 2021 suggested students had fallen four months behind in mathematics and three months behind in reading. 13 “ COVID-19 and education: An emerging K-shaped recovery ,” December 14, 2021. Inequalities in learning also increased within many of these countries, with historically marginalized students most affected.

Lower-income, prepandemic-challenged systems. This archetype consists of mostly low-income and lower-middle-income countries with very low levels of pre-COVID-19 learning. When the pandemic struck, school buildings closed for varying periods of time, 14 “Education: From disruption to recovery,” UNESCO, accessed March 11, 2022. with limited options for remote learning. In Tanzania, for example, schools were closed for 15 weeks, and during this period, just 6 percent of households reported that their children listened to radio lessons, 5 percent watched TV lessons, and fewer than 1 percent accessed educational programs on the internet. 15 Jacobus Cilliers and Shardul Oza, “What did children do during school closures? Insights from a parent survey in Tanzania,” Research on Improving Systems of Education (RISE), May 19, 2021. Across the analyzed time period, schools in sub-Saharan Africa were fully open for more weeks, on average, than schools in any other region. As a result, the pandemic’s impact on learning was relatively muted, even though many of these systems faced challenges with effective remote learning. 16 A report of six countries in Africa, for example, found limited impact of the pandemic on already-low student outcomes. For more information, see “MILO: Monitoring impacts on learning outcomes,” UNESCO, accessed March 11, 2022.

These relatively smaller pandemic learning delays are likely due in part to the limited progress students were making in schools before COVID-19. 17 World Bank blogs , “Harmonized learning outcomes: Transforming learning assessment data into national education policy reforms,” blog entry by Harry A. Patrinos and Noam Angrist, August 12, 2019. If students weren’t progressing scholastically when schools were open, closures were likely to have less impact. In Tanzania before the pandemic, three-quarters of students in grade three could not read a basic sentence. 18 “What did children do during school closures?,” May 19, 2021.

Pandemic-affected middle-income systems. School systems in Latin American and South Asian countries had low to moderate performance before COVID-19. Many middle-income countries in this group did have some capacity to plan and roll out remote-learning options, especially in urban areas. 19 “Responses to Educational Disruption Survey (REDS),” UNESCO, accessed March 11, 2022. However, pandemic-related disruptions caused widespread school closures for extended periods of time—more than 50 weeks in some countries. 20 “Education: From disruption to recovery,” UNESCO, accessed March 11, 2022. The resulting learning delays may represent a true crisis for major economies such as India, Indonesia, and Mexico, where students are more than a year behind, on average.

While some students may have just learned more slowly than they would have absent the pandemic, others in this archetype may have actually slipped backward. A study by the Azim Premji Foundation suggests that as early as January 2021, more than 90 percent of students assessed in India have lost at least one language ability (such as reading words or writing simple sentences), while more than 80 percent lost a math ability (for example, identifying single- and double-digit numbers or naming shapes). 21 Loss of learning during the pandemic , Azim Premji Foundation, February 2021. This pattern could be particularly challenging, since higher-order skills are increasingly important in middle-income countries with rising levels of workplace automation. McKinsey’s “ Jobs lost, jobs gained ” report 22 For more information, see “ Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages ,” McKinsey Global Institute, November 28, 2017. suggests India may need 34 million to 100 million more high school graduates by 2030 to fill workplace demands. The pandemic has put existing high school graduation rates at risk, let alone the vast expansion required to meet future demand for workers.

The pandemic’s effects beyond learning

Much of the dialogue around school systems focuses on educational achievement, but schools offer more than academic instruction. A school system’s contributions may include social interaction; an opportunity for students to build relationships with caring adults; a base for extracurricular activities, from the arts to athletics; an access point for physical- and mental-health services; and a guarantee of balanced meals on a regular basis. The school year may also enable students to track their progress and celebrate milestones. When schools had to close for extended periods of time or move to hybrid learning, students were deprived of many of these benefits.

The pandemic’s impact on the social-emotional and mental and physical health of students has been measured even less than its impact on academic achievement, but early indications are concerning. Save the Children reports that 83 percent of children and 89 percent of parents globally have reported an increase in negative feelings since the pandemic began. 23 The hidden impact of COVID-19 on child protection and wellbeing , Save the Children International, September 2020. In the United States, one in three parents said they were very or extremely worried about their child’s mental health in spring 2021, with rising reported levels of student anxiety, depression, social withdrawal, and lethargy. 24 Emma Dorn, Bryan Hancock, Jimmy Sarakatsannis, and Ellen Viruleg, “ COVID-19 and education: The lingering effects of unfinished learning ,” McKinsey, July 27, 2021. Parents of Black and Hispanic students, the segments most affected by academic unfinished learning, also reported higher rates of concern about their student’s mental health and engagement with school. A UK survey found 53 percent of girls and 44 percent of boys aged 13 to 18 had experienced symptoms or trauma related to COVID-19. 25 Report1: Impact of COVID-19 on young people aged 13-24 in the UK- preliminary findings , PsyArXiv, January 20, 2021. In Bangladesh, a cross-sectional study revealed that 19.3 percent of children suffered moderate mental-health impacts, while 7.2 percent suffered from extreme mental-health effects. 26 Rajon Banik et al., “Impact of COVID-19 pandemic on the mental health of children in Bangladesh: A cross-sectional study,” Children and Youth Services Review , October 2020, Volume 117. Reports of violence against children rose in many countries. 27 “Publications,” Young Lives, accessed March 22, 2022. The pandemic affected physical health as well. Studies from the United States 28 Roger Riddell, “CDC: Child obesity jumped during COVID-19 pandemic,” K-12 Dive , September 24, 2021. and the United Kingdom 29 The annual report of Her Majesty’s chief inspector of education, children’s services and skills 2020/21 , Ofsted, December 7, 2021. show rising rates of childhood obesity. In Latin America and the Caribbean, more than 80 million children stopped receiving hot meals. 30 “We can move to online learning, but not online eating,” United Nations World Food Program, March 26, 2020. In Uganda, a record number of monthly teenage pregnancies—more than 32,000—were recorded from March 2020 to September 2021. 31 “Uganda overwhelmed by 32,000 monthly teen pregnancies,” Yeni Şafak , December 12, 2021.

Some students may never return to formal schooling at all. Even in high-income systems, levels of chronic absenteeism are rising, and some students have not reengaged in school. In the United States, 1.7 million to 3.3 million eighth to 12th graders may drop out of school because of the pandemic. In low- and middle-income countries, the situation could be far worse. Up to one-third of Ugandan students may not return to the classroom. This pattern is in line with past historical crises involving school closures. After the Ebola pandemic, 13 percent of students in Sierra Leone and 25 percent of students in Liberia dropped out of school, with girls and low-income students most affected. 32 The socio-economic impacts of Ebola in Liberia , World Bank, April 15, 2015; The socio-economic impacts of Ebola in Sierra Leone , World Bank, June 15, 2015. Among the poorest primary-school students in Sierra Leone, dropout rates increased by more than 60 percent. 33 William C. Smith, “Consequences of school closure on access to education: Lessons from the 2013-2016 Ebola pandemic,” International Review of Education , April 2021, Volume 67. This may result in reduced employment opportunities and lifelong earnings potential for many of these students.

The potential of long-term economic damage

Education can affect not just an individual’s future earnings and well-being but also a country’s economic growth and vitality. Research suggests higher levels of education lead to increased labor productivity and enhance an economy’s capacity for innovation. Unless the pandemic’s impact on student learning can be mitigated and students can be supported to catch up on missed learning, the global economy could experience lower GDP growth over the lifetime of this generation.

We estimate by 2040, unfinished learning related to COVID-19 could translate to annual losses of $1.6 trillion to the global economy, or 0.9 percent of predicted total GDP (Exhibit 6).

Although the total dollar amount of forgone GDP is highest in the largest economies of the world (encompassing East Asia, Europe, and North America), the relative impact is highest in regions with the greatest learning delays. In Latin America and the Caribbean, pandemic-related school closures could result in losses of more than 2 percent of GDP annually by 2040 and in subsequent years.

Economic impact could be affected further if students don’t return to school and cease learning altogether.

Identifying potential solutions

The response to the learning crisis will likely vary from country to country, based upon preexisting educational performance, the depth and breadth of learning delays, and system resources and capacity to respond. That said, all school systems will likely need to plan across multiple horizons:

As 2022 began, more than 95 percent of school systems around the world were at least partially open for traditional in-person learning. 34 “Responses to Educational Disruption Survey (REDS),” UNESCO, 2022, accessed March 11, 2022. That progress is encouraging but tenuous. Many systems reopened only to close down again when another wave of COVID-19 caused additional disruptions. Even within partially open systems, not all students have access to in-person learning, and many are still attending partial days or weeks. Building resilience could mean ensuring protocols are in place for safe and supportive in-person learning, and ensuring plans are in place to provide remote options that support the whole child at the system, school, and student levels in response to future crises. School systems can also benefit by creating the flexibility to change policies and procedures as new data and circumstances arise.

COVID-19 and education: The lingering effects of unfinished learning

Reenrollment.

Opening buildings and embedding effective safety precautions have been challenging for many systems, but ensuring students and teachers actually turn up and reengage with learning is perhaps even more difficult. Even where in-person learning has resumed, many students have not returned or remain chronically absent. 35 Indira Dammu, Hailly T.N. Korman, and Bonnie O’Keefe, Missing in the margins 2021: Revisiting the COVID-19 attendance crisis , Bellwether Education Partners, October 21, 2021. Families may still have safety worries about in-person learning. Some students may have found jobs and now rely on that income. 36 Elias Biryabarema, “Student joy, dropout heartache as Uganda reopens schools after long COVID-19 shutdown,” Reuters, January 10, 2022. Others may have become pregnant or now act as caregivers at home. 37 Brookings Education Plus Development , “What do we know about the effects of COVID-19 on girls’ return to school?,” blog entry by Erin Ganju, Christina Kwauk, and Dana Schmidt, September 22, 2021. Still others may feel so far behind academically or so disconnected from the school environment at a social level that a return feels impossible. A multipronged approach could be helpful to understand the barriers students may face, how those could differ across student segments, and ways to support all students in continuing their educational journeys.

Systems could consider a tiered approach to support reengagement. Tier-one interventions could be rolled out for all students and include both improving school offerings for families and students and communicating about enhanced services. This might involve back-to-school awareness campaigns at the national and community levels featuring respected community members, clear communication of safety protocols, access to free food and other basic needs on campuses, and the promotion of a positive school climate with deep family engagement.

Tier-two interventions, which could be directed at students who are at heightened risk of not returning to school, may involve more targeted support. These efforts might include community events and canvassing to bring school buses or mobile libraries to historically marginalized neighborhoods, phone- or text-banking aimed at students who have not returned to school, or summer opportunities (including fun reorientation activities) to convince students to return to the school campus. At the student level, it could include providing some groups of students with deeper learning or social-emotional recovery services to help them reintegrate into school.

Tier-three interventions encompass more intensive and specialized support. These efforts may include visits to the homes of individual students or new educational environments tailored to student needs—for example, night schools for students who need to complete high school while working.

Once students are back in school, many may need support to recover from the academic and social-emotional effects of the pandemic. Indeed, while academic recovery seems daunting, supporting the mental-health and social-emotional needs of students may end up being the bigger challenge. 38 Protecting youth mental health: The U.S. surgeon general’s advisory , U.S. Department of Health and Human Services, 2021. This process starts with a recognition that each child is unique and that the pandemic has affected different students in different ways. Understanding each student’s situation, in terms of both learning and well-being, is important at the classroom level, with teachers and administrators trained to interpret cues from students and refer them to more intensive support when necessary. Assessments will likely also be needed at the school and system levels to plan the response.

With an understanding of both the depth and breadth of student needs, systems and schools could consider three levers of academic acceleration: more time, more dedicated attention, and more focused content. Implementation of these levers will likely vary by context, but the overall goals are the same: to overcome both historical gaps and new COVID-19-related losses, and to do so across academic and whole-child indicators.

In high-income countries, digital formative assessments could help determine in real time what students know, where they may have gaps, and what the next step could be for each child. More relational tactics can be incorporated alongside digital assessments, such as teachers taking the time to connect with each child around a simple reading assessment, which may rebuild relationships and connectivity while assessing student capabilities. Schools could also consider universal mental-health diagnostics and screeners, and train teachers and staff to recognize the signs of trauma in students.

Once schools have identified students who need academic support, proven, evidence-based solutions could support acceleration in high-income school systems. High-dosage tutoring, for example, could enable students to learn one to two additional years of mathematics in a single year. Delivered three to five times a week by trained college graduates during the school day on top of regular math instruction, this type of tutoring is labor and capital intensive but has a high return on investment. Acceleration academies, which provide 25 hours of targeted instruction in reading to small groups of eight to 12 students during vacations, have helped students gain three months of reading in just one week. Exposing students to grade-level content and providing them with targeted supports and scaffolds to access this content has improved course completion rates by two to four times over traditional “re-teaching” remediation approaches.

With an understanding of both the depth and breadth of student needs, systems and schools could consider three levers of academic acceleration: more time, more dedicated attention, and more focused content.

In low- and middle-income countries, where learning delays may have been much greater and where the financial and human-capital resources for education can be more limited, different implementation approaches may be required. Simple, fast, inexpensive, and low-stakes evaluations of student learning could be carried out at the classroom level using pen and paper, oral assessments, and mobile data collection, for example.

Solutions for supporting the acceleration of student learning in these contexts could start with ensuring foundational literacy and numeracy (FLN), prioritizing essential standards and content. Evidence-based teaching methods could speed up learning; for example, Pratham’s Teaching at the Right Level (TaRL) approach—which groups children by learning needs, rather than by age or grade, and dedicates time to basic skills with continual reassessment—has led to improvements of more than a year of learning in classrooms and summer camps. 39 Improvements of 0.2 to 0.7 standard deviations; assuming that one year of learning ranges from 0.2 of a standard deviation in low income countries and 0.5 of a standard deviation in high income countries, in accordance with World Bank assumptions:; João Pedro Azevedo et al., Simulating the potential impacts of COVID-19 school closures on schooling and learning outcomes , World Bank working paper 9284, June 2020; David K. Evans and Fei Yuan, Equivalent years of schooling , World Bank working paper 8752, February 2019. Even with the application of existing approaches, more time in class may be required—with options to extend the school year or school day to support students. Widespread tutoring may not be realistic in some countries, but peer-to-peer tutoring and cross-grade mentoring and coaching could supplement in-class efforts. 40 COVID-19 response–remediation: Helping students catch up on lost learning, with a focus on closing equity gaps , UNESCO, July 2020.

Reimagining

In addition to accelerating learning in the short term, systems can also use this moment to consider how to build better systems for the future. This may involve both recommitting to the core fundamentals of educational excellence and reimagining elements of instruction, teaching, and leadership for a post-COVID-19 world. 41 Jake Bryant, Emma Dorn, Stephen Hall, and Frédéric Panier, “ Reimagining a more equitable and resilient K-12 education system ,” McKinsey, September 8, 2020. A lot of ground could be covered by rolling out existing evidence-based interventions at scale—recommitting to core literacy and numeracy skills, high-quality instructional materials, job-embedded teacher coaching, and effective performance management. Recommitting to these basics, however, may not be enough. Systems can also innovate across multiple dimensions: providing whole-child supports, using technology to improve access and quality, moving toward competency-based learning, and rethinking teacher preparation and roles, school structures, and resource allocation.

For example, many systems are reemphasizing the importance of caring for the whole child. Integrating social-emotional learning for all students, providing trauma-informed training for teachers and staff, 42 “Welcome to the trauma-informed educator training series,” Mayerson Center for Safe and Healthy Children, accessed March 22, 2022. and providing counseling and more intensive support on and off campus for some students could provide supportive schooling environments beyond immediate crisis support. 43 “District student wellbeing services reflection tool,” Chiefs for Change, January 2022. A UNESCO survey suggests that 78 percent of countries offered psychosocial and emotional support to teachers as a response to the pandemic. 44 What’s next? Lessons on education recovery , June 2021. Looking forward, the State of California is launching a $3 billion multiyear transition to community schools, taking an integrated approach to students’ academic, health, and social-emotional needs in the context of the broader community in which those students live. 45 John Fensterwald, “California ready to launch $3 billion, multiyear transition to community schools,” EdSource, January 31, 2022.

The role of education technology in instruction is another much-debated element of reimagining. Proponents believe education technology holds promise to overcome human-capital challenges to improved access and quality, especially given the acceleration of digital adoption during the pandemic. Others point out that historical efforts to harness technology in education have not yielded results at scale. 46 Jake Bryant, Felipe Child, Emma Dorn, and Stephen Hall, “ New global data reveal education technology’s impact on learning ,” McKinsey, June 12, 2020.

Numerous experiments are under way in low- and middle-income countries where human capital  challenges are the greatest. Robust solar-powered tablets loaded with the evidence-based literacy and numeracy app one billion led to learning gains of more than four months 47 “Helping children achieve their full potential,” Imagine Worldwide, accessed March 22, 2022. in Malawi, with plans to roll out the program across the country’s 5,300 primary schools. 48 “Partners and projects,” onebillion.org, accessed March 22, 2022. NewGlobe’s digital teacher guides provide scripted lesson plans on devices designed for low-infrastructure environments. In Nigeria, students using these tools progressed twice as fast in numeracy and three times as fast in literacy as their peers. 49 “The EKOEXCEL effect,” NewGlobe Schools, accessed March 22, 2022. As new solutions are rolled out, it will likely be important to continually evaluate their impact compared with existing evidence-based approaches to retain what is working and discard that which is not.

Charting a potential path forward

There is no precedent for global learning delays at this scale, and the increasing automation of the workforce advances the urgency of supporting students to catch up to—and possibly exceed—prepandemic education levels to thrive in the global economy. Systems will likely need resources, knowledge, and organizational capacity to make progress across these priorities.

Even before COVID-19, UNESCO estimated that low- and middle-income countries faced a funding gap of $148 billion a year to reach universal preprimary, primary, and secondary education by 2030 as required by UN Sustainable Development Goal 4. As a result of the pandemic, that gap has widened to $180 billion to $195 billion a year. 50 Act now: Reduce the impact of COVID-19 on the cost of achieving SDG 4 , UNESCO, September 2020. Even if that funding gap were closed, the result would be increased enrollment, not improvements in learning. UNESCO estimates that just 3 percent of global stimulus funds related to COVID-19 have been directed to education , 97 percent of which is concentrated in high-income countries. 51 “Uneven global education stimulus risks widening learning disparities,” UNESCO, October 19, 2021.

In many countries, shortages of teachers and administrators are just as pressing as the lack of funding. Many teachers in Uganda weren’t paid during the pandemic and have found new careers. 52 Alon Mwesigwa, “’I’ll never go back’: Uganda’s schools at risk as teachers find new work during Covid,” Guardian , September 30, 2021. Even high-income countries are grappling with teacher shortages. In the United States, 40 percent of district leaders and principals describe their current staff shortages as “severe” or “very severe.” 53 Mark Lieberman, “How bad are school staffing shortages? What we learned by asking administrators,” EducationWeek , October 12, 2021. Fully addressing pandemic-related learning losses will require a full accounting of the cost and a long-term commitment, recognizing the critical importance of investments in education for future economic growth and stability.

Countries do not need to reinvent the wheel or go it alone. Many existing resources catalog evidence-based practices relevant to different contexts, both historical approaches and those specific to COVID-19 recovery. For high-income countries, the Education Endowment Foundation, Annenberg’s EdResearch for Recovery platform, and the Collaborative for Student Success resources for states and districts in the United States provide research-based guidance on solutions.

In many countries, shortages of teachers and administrators are just as pressing as the lack of funding.

For low- and middle-income countries, materials developed in partnership with UNESCO, UNICEF, and the World Bank include tools to support FLN, Continuous and Accelerated Learning, and teacher capacity (Teach and Coach). UNESCO’s COVID-19 Response Toolkit provides guidance across income levels. Collaboration across schools, regions, and countries could also promote knowledge sharing at a time of evolving needs and practices—from webinars to active communities of practice and shared-learning collaboratives.

Organizing for the response across these multiple levels is a challenge even for the most well-resourced and sophisticated systems. Our recent research found 80 percent of government efforts to transform performance don’t fully meet their objectives. 54 “ Delivering for citizens: How to triple the success rate of government transformations ,” McKinsey, May 31, 2018. Success will likely require a relentless focus on implementation and execution, with multiple feedback loops to achieve continuous learning and improvement.

The COVID-19 pandemic was indisputably a global health and economic crisis. Our research suggests it also caused an education crisis on a scale never seen before.

The pandemic also showed, however, that innovation and collaboration can arise out of hardship. The global education community has an opportunity to come together to respond, bringing evidence-based practices at scale to every classroom. Working together, donors and investors, school systems and districts, principals and teachers, and parents and families can ensure that the students who endured the pandemic are not a lost generation but are instead defined by their resilience.

Jacob Bryant is a partner in McKinsey’s Seattle office; Felipe Child is a partner in the Bogotá office, where Jose Espinosa is an associate partner; Emma Dorn is a senior expert in the Silicon Valley office; Stephen Hall is a partner in the Dubai office, where Dirk Schmautzer is a partner; Topsy Kola-Oyeneyin is a partner in the Lagos office; Cheryl Lim is a partner in the Singapore office; Frédéric Panier is a partner in the Brussels office; Jimmy Sarakatsannis is a senior partner in the Washington, DC, office; and Seckin Ungur is a partner in the Sydney office, where Bart Woord is an associate partner.

The authors wish to thank Annie Chen, Kunal Kamath, An Lanh Le, Sadie Pate, and Ellen Viruleg for their contributions to this article.

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Reimagining a more equitable and resilient K–12 education system

COVID-19 and education: An emerging K-shaped recovery

Teacher survey: Learning loss is global—and significant

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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COVID-19 and its impact on education, social life and mental health of students: A survey

Affiliation.

• 1 Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Bawana Road, Delhi 110042, India.
• PMID: 33390636
• PMCID: PMC7762625
• DOI: 10.1016/j.childyouth.2020.105866

The outbreak of COVID-19 affected the lives of all sections of society as people were asked to self-quarantine in their homes to prevent the spread of the virus. The lockdown had serious implications on mental health, resulting in psychological problems including frustration, stress, and depression. In order to explore the impacts of this pandemic on the lives of students, we conducted a survey of a total of 1182 individuals of different age groups from various educational institutes in Delhi - National Capital Region (NCR), India. The article identified the following as the impact of COVID-19 on the students of different age groups: time spent on online classes and self-study, medium used for learning, sleeping habits, daily fitness routine, and the subsequent effects on weight, social life, and mental health. Moreover, our research found that in order to deal with stress and anxiety, participants adopted different coping mechanisms and also sought help from their near ones. Further, the research examined the student's engagement on social media platforms among different age categories. This study suggests that public authorities should take all the necessary measures to enhance the learning experience by mitigating the negative impacts caused due to the COVID-19 outbreak.

Keywords: Children and Youth; Covid-19; Impact; Mental health; Online education; Students.

© 2020 Elsevier Ltd. All rights reserved.

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Conflict of interest statement

There is no conflict of interest.

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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