positive impact of covid 19 on education essay

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Introduction

The global outbreak of COVID-19 has certainly taken an overwhelming toll on everyone. People have lost their jobs, their homes, and even their lives. There is no getting past the fact that the overall impact on the world has been negative, but it is important to realize that positive aspects of the pandemic have been overshadowed by the many negative ones. In an attempt to slow the spread of the disease, many governments made the decision to implement lockdowns, forcing billions to work and take classes from home, in many cases for the first times in their lives. Not only have these lockdowns altered the way that people work and go to school, but they have altered the mental health of everyone and the environmental health of the world around us.

Connection to STS Theory

The positive impacts of technology during the pandemic stems from the Modernization Theory, posing that there is a relationship between societal and technological advancements as societies shift to become updated as opposed to traditional. Technology has brought about lots of resistance to COVID that would not have been possible without the drastic advancements in science over the years. Thanks to these advancements, relationships can stay connected, students can continue to learn, jobs can stay open, and the environment can subtly improve. Our modernized world is well enough suited to take on the troubling times that COVID-19 has brought along.

Technology with School – Relates to College Students

Remote learning has allowed each of us to learn from the comfort of our homes. Working remotely has also allowed us to work from our living rooms. The perks of both are not having to wake up early to drive to work in the mornings, not having to sit at an office desk for eight hours a day, and not having to walk to class. Working remotely and remote learning has also been a time saver for many individuals.

According to Business Insider, there are a few tips that will help students be successful while being virtual. One tip is to clean your workspace. It is important to have a space, just like you would at a desk in a classroom, to ensure that you are paying attention to the professor. It is always important to engage with your professor. It is important to contact your professor outside of the class section to ensure that you are retaining the information. Another tip that the Business Insider recommends is to connect with your classmates. It is vital to build connections with your classmates that will help everyone have a comfortable environment to ask questions.

Personal Growth

In March 2020, the COVID-19 outbreak hit the United States. College students were forced to leave their beloved campuses and go home to finish their semesters online. For some, it meant their schoolwork load was lightened and they could sleep until noon. For others, it meant their plans of graduating and having a job for the summer were in jeopardy. Regardless of their situation, one thing was likely the same for all: lots of time alone. Students found things to do to pass the time. Some learned to cook, some started exercising at home, and others had more time to do what they already loved.

Ethan, a student at the University of South Carolina, used the time to start lifting weights in his home gym. In the United States, sales of home gym equipment doubled, reaching nearly $2.4 Billion in revenue. Store shelves were entirely sold out of exercise equipment. Many students like Ethan report that exercising was one of the biggest changes they made during COVID lockdown.

Other students, such as Cam, found an opportunity to get in a better place mentally. “I learned not to take things for granted. My relationship with my family has gotten better. I’m a much stronger person,” the Clemson student reported. Grayson, an athlete at Winthrop University, reported that it made him have a more positive outlook on being by himself. A student that elected to remain anonymous was just happy they could wake up later and not have to brush their teeth as much because of masks. Whether a dentist would approve of that habit or not, an improvement in mental health is a win in anyone’s book.

A select few students decided to challenge themselves in a world where all odds are stacked against them.  Dean, a freshman at the University of South Carolina, decided to start his own bracelet and T-Shirt business in a time when small businesses all over the country were facing a grave threat of going out of business. All the while, he learned to play the guitar and uploaded his songs to SoundCloud, he reported.

Whether college students decided to get a six-pack or learned how to sew, almost everyone found something constructive and positive to do with their extra free time. The college students of COVID-19 learned what it meant to make the best of an unfortunate situation. Things may have looked bleak and frightening, but they learned how to manage those feelings and make something positive out of it.

Change in Workforce

Before the pandemic, many companies did not allow employees to work from home. Also, many companies would not even allow employees to take home items, such as laptops, as a safety precaution. According to Stanford Medicine, rapid innovation and implementation of technology has allowed for the employees to navigate the challenges. It states that it is clear that technology has transformed our typical daily workflow. Technology has also made it easier to connect with the patients during the pandemic.

The Pew Research Center states “about half of new teleworkers say they have more flexibility now and that majority who are working in person worry about virus exposure.” In December 2020, 71% of the workers that were surveyed were doing their job from home all or most of the time. Of those workers, more than half said if they were given the choice that they would want to keep working from home even after the pandemic. Among those who are currently working from home, most say that it has been easy to meet deadlines and complete projects on time without interruptions.

Environmental Improvements

Before the COVID-19 outbreak, a typical day consisted of billions of people across the globe commuting to work or school, whether that be through public buses or trains, driving themselves in cars, or some other means of transportation. As all these vehicles were used, immeasurable amounts of gases and chemicals were released into the atmosphere. As infection numbers and the death toll increased, most nations began enforcing lockdown protocols, and these mandates affected almost 3 billion people (Rume & Islam, 2020). Businesses and factories shut down or people began working from home, meaning they no longer needed to drive to work. In an attempt to stunt transmission, the majority of international travel was halted, limiting tourism, which also had a great impact. Since industrialization has advanced in major cities across the globe, the amount of Greenhouse Gases that have been emitted is alarming. Cars, buses, trains, industries, factories all release harmful chemicals due to the burning of fossil fuels or other energy sources. When these pollutants enter the atmosphere, they cause a variety of issues. It decreases overall air quality and visibility, and can be dangerous to those inhali ng the m.

According to research performed by Shakeel Ahmad Bhat and a group of other scientists from India, China, and the United Kingdom, Delhi, India is one of the most polluted cities in the world (Bhat et al, 2021). The city is highly industrialized and densely populated, contributing to the elevated levels of particulate matter in the air. Particulate matter is small pollutant liquid droplets and solid particles in the air (Environmental Protection Agency, 2020). When inhaled, they can burrow deep into the lungs and even the bloodstream and cause serious damage to a person, “particularly respiratory ailments” (Bhat et al, 2021). The two types of particulate matter are PM10 and PM2.5, and their numbers correspond to the size of the particles (their diameters in units of micrometers). The smaller the particle, the more harmful they are. By National Ambient Air Quality Standards (NAAQS), the level of particulate matter in Delhi is well above the tolerable limits. In 2016 alone, the amount of deaths caused by the poor air quality in India “was approximately 4.2 million” (Bhat et al, 2021).

Lockdowns positively affe cted more than just the air quality around the world; additionally, water quality and beaches were a major beneficiary. Tourism for centuries has led to a significant overuse of beach resources such as fishing and leisure activities, and these in turn led to pollution of the water. If people are using jet skis and boating in lakes or oceans, the fuel and exhaust often leak into the water which can cause significant harm to the wildlife that lives in it. Restricting beach access has allowed them to recover and regain their resources, and has also decreased the pollution levels in the water. The water flowing in the Venice canals are cleaner now than they have been before (Bhat et al, 2021). pH levels, electric conductivity, dissolved oxygen levels, biochemical oxygen demand, and chemical oxygen demand have all decreased as a result of the lockdowns (Rume & Islam, 2020). These decreases all contribute to the fact that overall water quality levels have increased.

Noise pollution is an often-overlooked type of pollution that affects the world, especially in highly urbanized regions. Noise pollution is elevated levels of sound which are typically caused by human activities including transportation, machines, factories, etc. When the noise levels are elevated for extended periods of time, it negatively affects all organisms in the area. It leads to hearing loss, lack of concentration, high stress levels, interrupted sleep, and many other issues in humans. As for the wildlife, their abilities to detect and avoid predators and prey are hindered by noise pollution. It affects the invertebrates responsible for the control of many environmental processes that maintain balance in the ecosystem (Rume & Islam, 2020). When lockdowns were implemented, traveling and transportation stopped, industries shut down, flights were canceled, and people stayed home. The environment was able to recover and the people and organisms within the ecosystem enjoy a higher quality of life as a result.

Reflection Questions

• What kinds of positive experiences have you had during the pandemic?
• As stated in the chapter, there are many students who spent their time working out or picked up new hobbies. What new things were you able to focus on during the lockdowns?

Bhat, Shakeel Ahmad et al. “Impact of COVID-Related Lockdowns on Environmental and Climate Change Scenarios.” Environmental research 195 (2021): 110839–110839. Web. https://www-sciencedirect-com.libproxy.clemson.edu/science/article/pii/S001393512100133X?via%3Dihub.

DiDonato, S., Forgo, E., & Manella, H. (2020, June 5). Here’s how technology is helping residents during the COVID-19 pandemic . Scope Blog. https://scopeblog.stanford.edu/2020/06/04/how-technology-is-helping-residents-during-the-covid-19-pandemic/.

Environmental Protection Agency. (2020, October 1). Particulate Matter (PM) Basics. EPA. https://www.epa.gov/pm-pollution/particulate-matter-pm-basics.

Merkle, Steffen. “Positive Experiences During COVID-19.” Survey. 18 April 2021.

Parker, K., Horowitz, J. M., & Minkin, R. (2021, February 9). How Coronavirus Has Changed the Way Americans Work . Pew Research Center’s Social & Demographic Trends Project. https://www.pewresearch.org/social-trends/2020/12/09/how-the-coronavirus-outbreak-has-and-hasnt-changed-the-way-americans-work/.

Rume, T., & Islam, S. M. D.-U. (2020, September 17). Environmental effects of COVID-19 pandemic and potential strategies of sustainability. Heliyon. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498239/#bib42.

Shaban, Hamza. “The Pandemic’s Home-Workout Revolution May Be Here to Stay.” The Washington Post, WP Company, 8 Jan. 2021, www.washingtonpost.com/road-to-recovery/2021/01/07/home-fitness-boom/.

Thompson, K. L. (2021, February 2). I’m a college professor who’s teaching virtually during the pandemic. Here are 7 things my most successful students do on Zoom. Business Insider. https://www.businessinsider.com/tips-for-zoom-success-as-remote-student-professor-advice-2021-2.

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Covid-19’s Impact on Students’ Academic and Mental Well-Being

The pandemic has revealed—and exacerbated—inequities that hold many students back. Here’s how teachers can help.

The pandemic has shone a spotlight on inequality in America: School closures and social isolation have affected all students, but particularly those living in poverty. Adding to the damage to their learning, a mental health crisis is emerging as many students have lost access to services that were offered by schools.

No matter what form school takes when the new year begins—whether students and teachers are back in the school building together or still at home—teachers will face a pressing issue: How can they help students recover and stay on track throughout the year even as their lives are likely to continue to be disrupted by the pandemic?

New research provides insights about the scope of the problem—as well as potential solutions.

The Achievement Gap Is Likely to Widen

A new study suggests that the coronavirus will undo months of academic gains, leaving many students behind. The study authors project that students will start the new school year with an average of 66 percent of the learning gains in reading and 44 percent of the learning gains in math, relative to the gains for a typical school year. But the situation is worse on the reading front, as the researchers also predict that the top third of students will make gains, possibly because they’re likely to continue reading with their families while schools are closed, thus widening the achievement gap.

To make matters worse, “few school systems provide plans to support students who need accommodations or other special populations,” the researchers point out in the study, potentially impacting students with special needs and English language learners.

Of course, the idea that over the summer students forget some of what they learned in school isn’t new. But there’s a big difference between summer learning loss and pandemic-related learning loss: During the summer, formal schooling stops, and learning loss happens at roughly the same rate for all students, the researchers point out. But instruction has been uneven during the pandemic, as some students have been able to participate fully in online learning while others have faced obstacles—such as lack of internet access—that have hindered their progress.

In the study, researchers analyzed a national sample of 5 million students in grades 3–8 who took the MAP Growth test, a tool schools use to assess students’ reading and math growth throughout the school year. The researchers compared typical growth in a standard-length school year to projections based on students being out of school from mid-March on. To make those projections, they looked at research on the summer slide, weather- and disaster-related closures (such as New Orleans after Hurricane Katrina), and absenteeism.

The researchers predict that, on average, students will experience substantial drops in reading and math, losing roughly three months’ worth of gains in reading and five months’ worth of gains in math. For Megan Kuhfeld, the lead author of the study, the biggest takeaway isn’t that learning loss will happen—that’s a given by this point—but that students will come back to school having declined at vastly different rates.

“We might be facing unprecedented levels of variability come fall,” Kuhfeld told me. “Especially in school districts that serve families with lots of different needs and resources. Instead of having students reading at a grade level above or below in their classroom, teachers might have kids who slipped back a lot versus kids who have moved forward.” 

Disproportionate Impact on Students Living in Poverty and Students of Color

Horace Mann once referred to schools as the “great equalizers,” yet the pandemic threatens to expose the underlying inequities of remote learning. According to a 2015 Pew Research Center analysis , 17 percent of teenagers have difficulty completing homework assignments because they do not have reliable access to a computer or internet connection. For Black students, the number spikes to 25 percent.

“There are many reasons to believe the Covid-19 impacts might be larger for children in poverty and children of color,” Kuhfeld wrote in the study. Their families suffer higher rates of infection, and the economic burden disproportionately falls on Black and Hispanic parents, who are less likely to be able to work from home during the pandemic.

Although children are less likely to become infected with Covid-19, the adult mortality rates, coupled with the devastating economic consequences of the pandemic, will likely have an indelible impact on their well-being.

Impacts on Students’ Mental Health

That impact on well-being may be magnified by another effect of school closures: Schools are “the de facto mental health system for many children and adolescents,” providing mental health services to 57 percent of adolescents who need care, according to the authors of a recent study published in JAMA Pediatrics . School closures may be especially disruptive for children from lower-income families, who are disproportionately likely to receive mental health services exclusively from schools.

“The Covid-19 pandemic may worsen existing mental health problems and lead to more cases among children and adolescents because of the unique combination of the public health crisis, social isolation, and economic recession,” write the authors of that study.

A major concern the researchers point to: Since most mental health disorders begin in childhood, it is essential that any mental health issues be identified early and treated. Left untreated, they can lead to serious health and emotional problems. In the short term, video conferencing may be an effective way to deliver mental health services to children.

Mental health and academic achievement are linked, research shows. Chronic stress changes the chemical and physical structure of the brain, impairing cognitive skills like attention, concentration, memory, and creativity. “You see deficits in your ability to regulate emotions in adaptive ways as a result of stress,” said Cara Wellman, a professor of neuroscience and psychology at Indiana University in a 2014 interview . In her research, Wellman discovered that chronic stress causes the connections between brain cells to shrink in mice, leading to cognitive deficiencies in the prefrontal cortex. 

While trauma-informed practices were widely used before the pandemic, they’re likely to be even more integral as students experience economic hardships and grieve the loss of family and friends. Teachers can look to schools like Fall-Hamilton Elementary in Nashville, Tennessee, as a model for trauma-informed practices . 

3 Ways Teachers Can Prepare

When schools reopen, many students may be behind, compared to a typical school year, so teachers will need to be very methodical about checking in on their students—not just academically but also emotionally. Some may feel prepared to tackle the new school year head-on, but others will still be recovering from the pandemic and may still be reeling from trauma, grief, and anxiety. 

Here are a few strategies teachers can prioritize when the new school year begins:

• Focus on relationships first. Fear and anxiety about the pandemic—coupled with uncertainty about the future—can be disruptive to a student’s ability to come to school ready to learn. Teachers can act as a powerful buffer against the adverse effects of trauma by helping to establish a safe and supportive environment for learning. From morning meetings to regular check-ins with students, strategies that center around relationship-building will be needed in the fall.
• Strengthen diagnostic testing. Educators should prepare for a greater range of variability in student learning than they would expect in a typical school year. Low-stakes assessments such as exit tickets and quizzes can help teachers gauge how much extra support students will need, how much time should be spent reviewing last year’s material, and what new topics can be covered.
• Differentiate instruction—particularly for vulnerable students. For the vast majority of schools, the abrupt transition to online learning left little time to plan a strategy that could adequately meet every student’s needs—in a recent survey by the Education Trust, only 24 percent of parents said that their child’s school was providing materials and other resources to support students with disabilities, and a quarter of non-English-speaking students were unable to obtain materials in their own language. Teachers can work to ensure that the students on the margins get the support they need by taking stock of students’ knowledge and skills, and differentiating instruction by giving them choices, connecting the curriculum to their interests, and providing them multiple opportunities to demonstrate their learning.

How is COVID-19 affecting student learning?

Subscribe to the brown center on education policy newsletter, initial findings from fall 2020, 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 beth tarasawa , bt beth tarasawa executive vice president of research - nwea angela johnson , aj angela johnson research scientist - nwea erik ruzek , and er erik ruzek research assistant professor, curry school of education - university of virginia karyn lewis karyn lewis director, center for school and student progress - nwea.

December 3, 2020

The COVID-19 pandemic has introduced uncertainty into major aspects of national and global society, including for schools. For example, there is uncertainty about how school closures last spring impacted student achievement, as well as how the rapid conversion of most instruction to an online platform this academic year will continue to affect achievement. Without data on how the virus impacts student learning, making informed decisions about whether and when to return to in-person instruction remains difficult. Even now, education leaders must grapple with seemingly impossible choices that balance health risks associated with in-person learning against the educational needs of children, which may be better served when kids are in their physical schools.

Amidst all this uncertainty, there is growing consensus that school closures in spring 2020 likely had negative effects on student learning. For example, in an earlier post for this blog , we presented our research forecasting the possible impact of school closures on achievement. Based on historical learning trends and prior research on how out-of-school-time affects learning, we estimated that students would potentially begin fall 2020 with roughly 70% of the learning gains in reading relative to a typical school year. In mathematics, students were predicted to show even smaller learning gains from the previous year, returning with less than 50% of typical gains. While these and other similar forecasts presented a grim portrait of the challenges facing students and educators this fall, they were nonetheless projections. The question remained: What would learning trends in actual data from the 2020-21 school year really look like?

With fall 2020 data now in hand , we can move beyond forecasting and begin to describe what did happen. While the closures last spring left most schools without assessment data from that time, thousands of schools began testing this fall, making it possible to compare learning gains in a typical, pre-COVID-19 year to those same gains during the COVID-19 pandemic. Using data from nearly 4.4 million students in grades 3-8 who took MAP ® Growth™ reading and math assessments in fall 2020, we examined two primary research questions:

• How did students perform in fall 2020 relative to a typical school year (specifically, fall 2019)?
• Have students made learning gains since schools physically closed in March 2020?

To answer these questions, we compared students’ academic achievement and growth during the COVID-19 pandemic to the achievement and growth patterns observed in 2019. We report student achievement as a percentile rank, which is a normative measure of a student’s achievement in a given grade/subject relative to the MAP Growth national norms (reflecting pre-COVID-19 achievement levels).

To make sure the students who took the tests before and after COVID-19 school closures were demographically similar, all analyses were limited to a sample of 8,000 schools that tested students in both fall 2019 and fall 2020. Compared to all public schools in the nation, schools in the sample had slightly larger total enrollment, a lower percentage of low-income students, and a higher percentage of white students. Since our sample includes both in-person and remote testers in fall 2020, we conducted an initial comparability study of remote and in-person testing in fall 2020. We found consistent psychometric characteristics and trends in test scores for remote and in-person tests for students in grades 3-8, but caution that remote testing conditions may be qualitatively different for K-2 students. For more details on the sample and methodology, please see the technical report accompanying this study.

In some cases, our results tell a more optimistic story than what we feared. In others, the results are as deeply concerning as we expected based on our projections.

Question 1: How did students perform in fall 2020 relative to a typical school year?

When comparing students’ median percentile rank for fall 2020 to those for fall 2019, there is good news to share: Students in grades 3-8 performed similarly in reading to same-grade students in fall 2019. While the reason for the stability of these achievement results cannot be easily pinned down, possible explanations are that students read more on their own, and parents are better equipped to support learning in reading compared to other subjects that require more formal instruction.

The news in math, however, is more worrying. The figure below shows the median percentile rank in math by grade level in fall 2019 and fall 2020. As the figure indicates, the math achievement of students in 2020 was about 5 to 10 percentile points lower compared to same-grade students the prior year.

Figure 1: MAP Growth Percentiles in Math by Grade Level in Fall 2019 and Fall 2020

Source: Author calculations with MAP Growth data. Notes: Each bar represents the median percentile rank in a given grade/term.

Question 2: Have students made learning gains since schools physically closed, and how do these gains compare to gains in a more typical year?

To answer this question, we examined learning gains/losses between winter 2020 (January through early March) and fall 2020 relative to those same gains in a pre-COVID-19 period (between winter 2019 and fall 2019). We did not examine spring-to-fall changes because so few students tested in spring 2020 (after the pandemic began). In almost all grades, the majority of students made some learning gains in both reading and math since the COVID-19 pandemic started, though gains were smaller in math in 2020 relative to the gains students in the same grades made in the winter 2019-fall 2019 period.

Figure 2 shows the distribution of change in reading scores by grade for the winter 2020 to fall 2020 period (light blue) as compared to same-grade students in the pre-pandemic span of winter 2019 to fall 2019 (dark blue). The 2019 and 2020 distributions largely overlapped, suggesting similar amounts of within-student change from one grade to the next.

Figure 2: Distribution of Within-student Change from Winter 2019-Fall 2019 vs Winter 2020-Fall 2020 in Reading

Source: Author calculations with MAP Growth data. Notes: The dashed line represents zero growth (e.g., winter and fall test scores were equivalent). A positive value indicates that a student scored higher in the fall than their prior winter score; a negative value indicates a student scored lower in the fall than their prior winter score.

Meanwhile, Figure 3 shows the distribution of change for students in different grade levels for the winter 2020 to fall 2020 period in math. In contrast to reading, these results show a downward shift: A smaller proportion of students demonstrated positive math growth in the 2020 period than in the 2019 period for all grades. For example, 79% of students switching from 3 rd to 4 th grade made academic gains between winter 2019 and fall 2019, relative to 57% of students in the same grade range in 2020.

Figure 3: Distribution of Within-student Change from Winter 2019-Fall 2019 vs. Winter 2020-Fall 2020 in Math

It was widely speculated that the COVID-19 pandemic would lead to very unequal opportunities for learning depending on whether students had access to technology and parental support during the school closures, which would result in greater heterogeneity in terms of learning gains/losses in 2020. Notably, however, we do not see evidence that within-student change is more spread out this year relative to the pre-pandemic 2019 distribution.

The long-term effects of COVID-19 are still unknown

In some ways, our findings show an optimistic picture: In reading, on average, the achievement percentiles of students in fall 2020 were similar to those of same-grade students in fall 2019, and in almost all grades, most students made some learning gains since the COVID-19 pandemic started. In math, however, the results tell a less rosy story: Student achievement was lower than the pre-COVID-19 performance by same-grade students in fall 2019, and students showed lower growth in math across grades 3 to 8 relative to peers in the previous, more typical year. Schools will need clear local data to understand if these national trends are reflective of their students. Additional resources and supports should be deployed in math specifically to get students back on track.

In this study, we limited our analyses to a consistent set of schools between fall 2019 and fall 2020. However, approximately one in four students who tested within these schools in fall 2019 are no longer in our sample in fall 2020. This is a sizeable increase from the 15% attrition from fall 2018 to fall 2019. One possible explanation is that some students lacked reliable technology. A second is that they disengaged from school due to economic, health, or other factors. More coordinated efforts are required to establish communication with students who are not attending school or disengaging from instruction to get them back on track, especially our most vulnerable students.

Finally, we are only scratching the surface in quantifying the short-term and long-term academic and non-academic impacts of COVID-19. While more students are back in schools now and educators have more experience with remote instruction than when the pandemic forced schools to close in spring 2020, the collective shock we are experiencing is ongoing. We will continue to examine students’ academic progress throughout the 2020-21 school year to understand how recovery and growth unfold amid an ongoing pandemic.

Thankfully, we know much more about the impact the pandemic has had on student learning than we did even a few months ago. However, that knowledge makes clear that there is work to be done to help many students get back on track in math, and that the long-term ramifications of COVID-19 for student learning—especially among underserved communities—remain unknown.

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The Impact of the COVID-19 Pandemic on Education Learning

• First Online: 15 November 2023

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• Ismael Sanz 3 &
• J. D. Tena 4 , 5  

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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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• Published: 30 January 2023

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

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

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

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

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

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

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

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

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

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

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

The state of the evidence

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

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

The geographic reach of evidence is limited

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

The quality of evidence is mixed

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

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

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

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

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

No evidence of publication bias

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

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

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

Learning progress slowed substantially during the pandemic

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

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

Learning deficits arose early in the pandemic and persist

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

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

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

Socio-economic inequality in education increased

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

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

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

Learning deficits are larger in maths than in reading

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

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

No evidence of variation across grade levels

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

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

Learning deficits are larger in poorer countries

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

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

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

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

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

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

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

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

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

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

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

Eligibility criteria

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

Search strategy and study identification

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

Data extraction

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

Measurement and standardizationr

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

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

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

Level of education

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

Country income level

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

Data synthesis

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

Pre-registration

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

Reporting summary

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

Data availability

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

Code availability

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

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Acknowledgements

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

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Betthäuser, B.A., Bach-Mortensen, A.M. & Engzell, P. A systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic. Nat Hum Behav 7 , 375–385 (2023). https://doi.org/10.1038/s41562-022-01506-4

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Seven positive outcomes of COVID-19

COVID-19 has had undeniable and horrific consequences on people’s lives and the economy. With sickness, death and unemployment rates soaring almost everywhere on our planet, it is easy to despair.

Notwithstanding the gruesomeness of this situation, there are some outcomes that could have a long-term positive impact on the planet and humanity.

1. The Environment

The first positive aspect of COVID-19 is the effect on the environment .  Carbon emissions are down globally and with manufacturing and air travel grinding to a halt, the planet has had a chance to rejuvenate.

China recorded an 85 per cent increase in days with good air quality in 337 cities between January and March. With tourists gone from Italy, the long-polluted canals of Venice now appear clear as fish and other wildlife start returning. Elsewhere, wildlife is also reappearing in other major cities and the biodiversity is slowly starting to return in various parts of the world.

The coronavirus is also raising hopes of fewer battles and less conflict, resulting in increased levels of peace. The United Nations called to end all wars in the face of COVID-19 as the world confronts a common enemy: “It’s time to put armed conflict on lockdown,” stated Secretary-General António Guterres.

So many businesses have had to reinvent themselves with a new 'business as unusual' philosophy.

And according to the ABC, a ceasefire was declared by the Saudis fighting Houthi rebels in Yemen. Although there are many places in the Middle East where war persists, a stronger lockdown could lead to less violence in these countries too.

3. Connectedness

A third positive outcome is a rejuvenated sense of community and social cohesion. Self-isolation challenges us as social animals who desire relationships, contact and interaction with other humans.

However, people all around the world are finding new ways to address the need for interconnectedness.  In Italy, one of the worst-hit countries, people are joining their instruments and voices to create music from their balconies . People are leading street dance parties while maintaining social distancing.

People are using social media platforms to connect, such as the Facebook group The Kindness Pandemic , with hundreds of daily posts. There is a huge wave of formal and informal volunteering where people use their skills and abilities to help.

4. Innovation

COVID-19 is a major market disruptor that has led to unprecedent levels of innovation. Due to the lockdown, so many businesses have had to reinvent themselves with a new 'business as unusual' philosophy.

This includes cafes turning into takeaway venues (some of which also now sell milk or face masks) and gin distilleries now making hand sanitisers .

Many businesses have had to undergo rapid digitalisation and offer their services online. Some could use this wave of innovation to reimagine their business model and change or grow their market.

5. Corporate Responsibility

Coronavirus is driving a new wave of corporate social responsibility (CSR). The global pandemic has become a litmus test for how seriously companies are taking their CSR and their work with key stakeholders: the community, employees, consumers and the environment.

Home-schooling is becoming the new way of learning, exposing many parents to what their children know and do.

Companies are donating money, food and medical equipment to support people affected by the coronavirus. Others are giving to healthcare workers, including  free coffee at McDonald’s Australia and millions of masks from  Johnson & Johnson .

Many are supporting their customers, from Woolworths introducing an exclusive shopping hour for seniors and people with disabilities to Optus giving free mobile data so its subscribers can continue to connect.

6. Reimagined Education

The sixth positive outcome is massive transformation in education. True, most of it was not by choice. With schools closing down all around the world, many teachers are digitalising the classroom , offering online education, educational games and tasks and self-led learning.

Silver linings amid the suffering: Professor Debbie Haski-Leventhal believes a new found sense of gratitude for freedoms we take for granted and a global trend in thanking health workers who are at the frontline are among the positives to come out of the crisis.

We are globally involved in one of the largest-scale experiments in changing education at all levels. Home-schooling is becoming the new way of learning, exposing many parents to what their children know and do.

Similarly, universities are leading remote learning and use state-of-the-art solutions to keep students engaged. Some universities are using augmented and virtual reality to provide near real-life experiences for galvanising students’ curiosity, engagement and commitment and for preparing students for the workplace.

7. Gratitude

Finally, the seventh gift that COVID-19 is giving us is a new sense of appreciation and gratefulness . It has offered us a new perspective on everything we have taken for granted for so long – our freedoms, leisure, connections, work, family and friends. We have never questioned how life as we know it could be suddenly taken away from us.

Hopefully, when this crisis is over, we will exhibit new levels of gratitude . We have also learned to value and thank health workers who are at the frontline of this crisis, risking their lives everyday by just showing up to their vital work. This sense of gratefulness can also help us develop our resilience and overcome the crisis in the long-term.

• Will a vaccine really solve our COVID-19 woes?
• How Sydney has coped with pandemics in the past

All of these positive aspects come at a great price of death, sickness and a depressed global economy. As heartbreaking and frightening as this crisis is, its positive outcomes can be gifts we should not overlook. If we ignore them, all of this becomes meaningless.

It will be up to us to change ourselves and our system to continue with the positive environmental impact, peace, connectedness, innovation, corporate responsibility, reimagined education and gratitude. This crisis will end. We will meet again. We can do so as better human beings.

Debbie Haski-Leventhal is a Professor of Management at the Macquarie Business School. She is a TED speaker and the author of Strategic Corporate Social Responsibility: Tools and Theories for Responsible Management and The Purpose-Driven University .

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The Positive Effects of COVID-19 on Education

In my last post I explored how this global pandemic has had negative impacts on learning and education in America, so this week I decided to look into the opposite idea. Today, I want to look into some of the  positive effects.

Students and educators alike have adjusted to learning remotely, which has greatly improved typing skills, computer awareness, and even small things such as email etiquette. Although students that are younger have faced more challenges navigating their education online, they have overcome these obstacles and learned  new skills that will be very important as they grow older.

According to this  article, not only are students and teachers becoming more aware of how to help each other and stay on top of work, they are all learning how to shift priorities a bit to make a better environment to learn in. With the pandemic, many families faced new hardships that they were unprepared for. In this newly technological world, people are beginning to emphasize the importance of students’ mental health and wellb

eing, as well as that of educators alike. Conversations are easier, resources are more readily accessible, and communication between students and teachers and parents is offered through more options than ever before.

Resources such as Zoom and

Although there are so many negatives about COVID-19, taking the time to look at the positives is extremely important. Building better relationships inside and outside the classroom thanks to new online resources will help prepare young students for their futures, and will allow educators to help on a more personal level when needed. I know that for me personally, just having the ability to Zoom into high school classes on days I was sick helped me feel a little less behind and more on top of my workload, which helped me feel better in other aspects of my life as well.

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Research Article

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

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

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

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

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

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

Roles Data curation, Writing – original draft

Roles Data curation

Roles Writing – original draft

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

Roles Conceptualization, Supervision, Writing – review & editing

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

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

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

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

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

Editor: Heng Luo, Central China Normal University, CHINA

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

Copyright: © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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

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

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

1. Introduction

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

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

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

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

2. Literature review and research questions

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

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

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

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

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

3. Methodology

3.1 research context.

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

3.2 Data collection and preprocessing

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

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

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

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

3.3 Instrumentation

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

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

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

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

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

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

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

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

3.4 Ethics statement

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

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

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

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

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

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

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

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

4.2 Characteristics of online learning interaction amid COVID-19

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

4.3 Analyses of natural science courses and social science courses

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

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

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

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

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

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

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

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

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

5. Discussion

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

6. Conclusion and future work

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

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

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Positive and negative impacts of COVID-19, an analysis with special reference to challenges on the supply chain in South Asian countries

K. karunathilake.

1 Department of Sociology, University of Kelaniya, Kelaniya, Sri Lanka

2 Centre for Study Social Systems, School of Social Sciences, Jawaharlal Nehru University, New Delhi, India

The Coronavirus or COVID-19 is a disease based on an unknown virus. It seems that it started in China and has widely spread in almost all countries in the world. This pandemic situation is one of the widely spread diseases in recent history. However, there was an influenza pandemic in 1918 with the exact number of deaths still unknown. Some believe that the death toll would have been about 50–100 million people. At the time of writing this article, COVID-19 has infected 5,306,928 persons worldwide (when the article was finalised for publication, the number has increased up to 15,947,291). The article is aimed at analysing the positive and negative impacts of COVID-19 in a sociological perspective. It is further focused on possible challenges to the supply chain in South Asia. South Asian countries are highly influenced by the pandemic situation, and the regional representation is about 4% in the later part of May 2020 with an increasing tendency. Also, the article has a proposal for the control of the disease as well as the entire socio-economic, environmental and political atmosphere in a country, whilst particularly giving more weight to South Asia. The proposed actions are analysed in short-term, mid-term and long-term basis, and any expert and social worker who is involved in the pandemic control process can gain an insight into what to do and how to perform their tasks. A sociological analysis on COVID-19 is very important because there is a wing comprising dominant medical experts in the control and management of the disease. The article emphasises the importance of a sociological analysis in a pandemic situation. Naturally, anyone would think of a pandemic situation in very negative terms due to its emotional, socio-economic, environmental, political and cultural factors. However, it is also positive due to certain factors that help to reintegrate and reorganise the social system as a whole.

The main objective of this paper is to discuss the positive and negative impacts of COVID-19 in a sociological perspective with special attention to the supply change in South Asian countries. In addition, the paper proposes a future action plan or COVID-19 recovery action plan for Sri Lanka as a South Asian country. The country is in a COVID19 affected stage and is gradually progressing into a non-affected stage, and this action plan is focused on at least a five-year period.

There is no argument that the local economic and social spheres as well as global spheres have been challenged and some sectors have been drastically dismantled (Malpass 2020 ). Therefore, we need to repose our trust mostly on local spheres which facilitate our basic needs and other socio-economic needs structure. As we know very well, we are in possession of a substantial service sector, rather than industrial and agricultural sectors. In contrast to these sectors, we need to maintain sound industrial and agricultural sectors to maintain a good service sector. However, our industrial sector will be severely damaged due to COVID-19. Especially, the apparel and textile industry will be heavily damaged due to COVID-19, since the pandemic has severely affected giant countries such as the USA and European countries. This will lead to lesser and lesser demand for readymade apparels, thus leading to the closure of many apparel industries in the country. The factories would not be able to overcome this situation since their buyers would cut down all possible avenues to recover from the dire situation. The export volumes of the agricultural and fisheries sectors would decrease, creating negative economic and social impacts on Sri Lanka. Though the World Bank issued a statement on March 30, 2020, it does not provide any forecasts on Sri Lanka or South Asian countries. It focuses only on East Asian and Pacific countries whilst highlighting the poverty incident that would increase dramatically (World Bank 2020c ). If the economic situation were to deteriorate further and the lower-case scenario prevails, then poverty is estimated to increase by about 11 million people in these regions. In April 2020, the World Bank illustrates the South Asian context of COVID-19 and its impacts. It says that the impact of the pandemic will hit hard low-income people, especially informal workers in the hospitality, retail trade and transport sectors who have limited or no access to healthcare or social safety nets (World Bank 2020a ). Furthermore, it focuses that the Sri Lankan economic growth in the first quarter of 2020 is between 3.0 and − 0.5 and it will remain low throughout the year. It may slowly grow until 2022 with a 2.5% growth rate. The forecast analysis shows that Sri Lanka will struggle with the economy. Thus, the country needs a well-integrated plan to avoid economic hardships as well as social and political tensions. Especially, it needs political stability, high level of mass participation in all sectors, attitude change in domestic production and use, and low level of dependency on foreign aids and foreign goods and services. Especially, it will help to refurbish the domestic supply chain as the countries’ primary target.

When the Sri Lankan scenario is looked at, the economic growth has translated into shared prosperity with the national poverty headcount ratio declining from 15.3% in 2006/07 to 4.1% in 2016 (World Bank 2020b ). Extreme poverty is rare and concentrated within some geographical pockets; however, a relatively large share of the population subsists on slightly more than the poverty line. However, COVID-19 would increase poverty incidents to some extent since many apparel sector workers are unemployed or under-employed. Foreign migrant labourers are affected due to the situations in their respective countries and they are losing their decent income. Simultaneously, the tourism and hotel industry will be severely affected, sometimes temporarily as Sri Lanka is managing the pandemic situation satisfactorily and this could attract future foreign tourists on the lookout for healthy living. If the Government of Sri Lanka (GOSL) plans a well-integrated domestic production, strengthening and improvement activities, it will ensure the flow of the national supply chain without any disturbances, and also such a situation may help to gain significant improvements in local and foreign tourism.

Within the socio-economic background highlighted above, Sri Lanka needs to have a very comprehensive future action plan to overcome all negative impacts. Especially, it is important to identify the short-term, medium-term, and long-term negative and positive impacts in terms of developing a future action plan or COVID-19 recovery action plan.

When focusing on positive impacts the immediate question that may arise is, what are the positive impacts in a global pandemic situation? Sociologically, there is a theoretical perspective and a method called functionalism or functional perspective and this method could be utilised in analysing the impact of social action (Parsons 1951a , b ; Rocher 1972 ; Luhmann 1995 ; Knudsen 2010 ). COVID-19 is a social action, and it can also be considered a great social problem on the basis of Richard Puller’s definition. When looked at generally, COVID-19 is a disease spreading through close human contacts in day-to-day social relationships. It is a virus and certain sections also consider this to be a man-made virus or biological weapon. There is no conclusive evidence on who made it or for what purpose. The majority of those directly affected are the elderly and a considerable number of people are dying. The percentage is changing from country to country, and the USA which is highly affected shows that 2.9% of the deaths are amongst these affected persons. The death toll is very high in Italy which is 13%. In Sri Lanka, it is 3.7% and it is a higher figure when compared to India, which is 2.8% ( https://www.worldometers.info/coronavirus/#countries , 2020 ). Thus, it is important to understand what the positive impacts are. They are given in the following:

• People are adapting to a pandemic situation, and they also understand what the ideal social behaviour is in a similar situation. This may include government pandemic management systems and policies.
• They learnt about social distancing and its rules, conditions and procedures. Especially, how painful it is but useful within the family and the community. Also, they adapt themselves to the situation whilst contemplating the difference between a normal situation and a pandemic situation.
• As a result of social integration, families and communities engage in a high level of social cohesion or social conscience to face the difficult situation. Hence, everyone is getting used to a common lifestyle, sharing and caring for others, especially the elderly people.
• The death of an elderly or chronically ill person results in a reorganisation or reunion of the family unit. Though it is not an easy task to bear the psycho-social factors, finally they come to certain common conclusions.
• Similarly, the government and regional organisations in the global context may reunite to face the pandemic situation. For example, the SARRC countries reunited over COVID-19 and set up financial allocations for supporting poorer nations. The recent SAARC video conference on COVID 19 has resulted in the establishment of a fund for regional cooperation to combat the pandemic. The giant country India contributed USD 10 million followed by Sri Lanka which contributed USD 5 to the fund. This fund can be utilised for the improvement of domestic and regional production whilst aiming at an uninterrupted supply chain in the agricultural, industrial and service sectors.
• Medical systems in any country will be improved to sustainable levels to face a pandemic situation. They will understand the existing gaps in these systems. Especially, many South and East Asian countries will focus on their indigenous medical systems and how it should be integrated with the biomedical system which shows poorer results. Their dominant authority may be challenged by indigenous medical practitioners due to the strength of the indigenous medical systems to treat COVID-19 patients.
• The global hegemonic power would change and a new hegemonic power relation is taking its place without any brutal war conditions and much economic and social costs. It seems that the USA may lose the hegemonic power, which would be replaced by China and within such a scenario China could achieve its long-term goals. However, it can be delayed and disturbed by the USA through certain economic restrictions against China. Recently, the USA announced that they are willing to withdraw their investments in China. Under such circumstances South Asian countries, particularly India can offer much better economic policies to attract US investments in India. Similarly, Sri Lanka and Bangladesh can grab some such opportunities in certain industrial fields. This may help in improving supply chain values and efficiency in South Asian countries.
• The new hegemonic power may extend its supporting hands to the developing nations and poorer countries. Thus, there will be a competition amongst China, USA and India to support regional countries in South East Asia. The new world order may be a novel experience to the countries and their people. At the beginning it will be optimistic as per the conflict theory in sociology. If it suits the supply chain system in these countries then the people may accept the new order.
• There will be many discoveries and innovations in all affected sectors or spheres at national, regional and global contexts. Most of these would be medical, environment, industry and socio-cultural related. These discoveries and innovations will help to manage the supply chain in South Asian countries.
• The potentials would be in domestic production and services to maintain local traditional lifestyles rather than adapting to modernity. People may repose their trust in many local-level trades and business firms rather than depending on the supermarket systems. These trends strengthen the supply chain network at national and regional levels. Perhaps, there may be some new trading opportunities too amongst regional countries.
• Domestic production could increase due to family or cottage level agricultural practices including other small-scale handicraft productions in society. This may reduce the market demand to some extent and it may also cause negative effects amongst international trades.
• As mentioned above, the pandemic situation may increase the level of innovations amongst the people. Some creative-minded people may introduce much effective and productive primary and secondary things, efficient ways and means of productions, low-cost productions, technological advancements, etc. These trends too may strengthen the supply chain network at national and regional levels.
• People may be attracted to traditional foods and consumption practices mainly based on the human family. Intergenerational social integrity will strengthen and be consolidated by youths. There will be a lesser demand for restaurants and hotel sector supply chains in each level such as national, regional and global due to the social and physical distancing.
• Decline in defence expenditure at national and global levels and minimising the need or requirements in the arms race, especially in nuclear weaponry systems, at least on a temporary basis. Thus, war fear could be minimised in certain regions. This may in turn influence international terrorism and its supply chain networks.
• New ideological constructions in many critical subject disciplines, such as medicine, economics, political science, sociology, psychology, robotic sciences, religious and humanistic sciences. Hence, research and development activities will expand in every country.
• Developments in sociological tool-kits and social engineering skills to deliver efficient services through supply chain networks and management of patients and general public.
• People getting used to do some optional analysis and adapt to follow optimum use of resources, economising resources, and sustainable approaches to satisfy their need structure. These trends may reorganise the supply chain networks in national and regional levels.
• General public may develop some positive altitudes over the special duties performed by various essential services in society, especially, health workers, social workers, police and armed forces who are involved in quarantine processes, etc. However, there should be very efficient supply chains to maintain these services in optimum operation when necessary to society, particularly, in South Asia.
• Reduction in plastic and polythene use at the domestic level, thus reducing the environmental pollution. As a whole, it may contribute to global environmental protection efforts. Especially, it may temporarily reduce greenhouse gases due to the minimum use of vehicles and industries in the world. Perhaps, there may be some climate changes in the environment.
• Reduction in the environmental pollution in the world, regional and national contexts. Mainly, air, sound, and water pollution will be reduced. There are some studies conducted, and they reveal that the pollution level is reduced drastically in all these three sectors.
• Possible formation of further philanthropic ideas amongst the upper hierarchy segments in society. Thus, the domestic supply chain networks should capture these demands effectively.
• If the government is successful in managing the COVID-19 instead of developing a pandemic situation, it can lead to a stable political order in society. Perhaps, the incumbent government could win another term in power as the pandemic situation has provided a great opportunity to compare each country and its skills in the governance of their political regimes.
• Reduction in crimes such as drug addiction, alcoholism, gambling, prostitution or commercial sex, violence and suicide. Again, the supply chain networks in commercial sex, illicit drugs, etc. may change.
• Some countries may amend or introduce specific legislation based on their experiences in the management of COVID-19 pandemic situations. Particularly, trading pacts amongst regional countries may change whilst new pacts may also be formulated.
• Some countries may review policy gaps and take action for policy updates. Sri Lanka needs a policy on indigenous medicine (traditional medicine) which is not covered by the existing health policy in Sri Lanka. It is the right time to respond to the court decision and request a new pharmacopeia with a list of codes.
• More utilisation of the internet for sharing ideas, new knowledge, filtering knowledge gaps, news messages, etc. This may open up more avenues to reduce the stress level of the people.
• Return migrants who are having strong financial resources may invest in Sri Lanka if the socio-economic and political stability established after complete control and management of COVID-19 in Sri Lanka.
• Returning migrants with less or poor financial resources may seek jobs in the apparel sector or plantation sector. Therefore, there will be a high supply in the labour market in the country. This situation may impact the new formation of production and supply chain networks.
• There will be more research on COVID-19, particularly discovering a vaccine and medical strategies which are more effective in patient management. If any new vaccinations are discovered, there will be a massive demand from each country and new supply chain networks will emerge within the health sector.

Therefore, positive impacts are relatively high and some impacts are short term and most others are long term. This situation depends on the early stage of April 2020 and the shape could change due to several global socio-economic and political factors. So far, COVID-19 is in a rapid spread tendency in most countries, and there are no sufficient health and socio-economic facilities, especially supply chain networks to address the need structure of the pandemic situation in these countries. When the Indian scenario is analysed, the COVID-19 impacts will be the most painful here when compared to other countries. The social system prevailing in India is not simple in terms of managing the pandemic situation. It is very clear that any society which has a high social disparity may have to face the most negative impacts.

The negative impacts towards the family, communities, nations, regions and the world push them backward in any sector or socio-economic and political spheres. Several elements which cause negative impacts can be identified as illnesses or COVID-19, pandemic situation, deaths, social distancing, curfew and the lock-down of the entire functional mechanism of a single society and the global network in production, trade, supply chain networks, transportation, social networking and political network. Therefore, this paper has given similar attention to the negative impacts of COVID-19 at the local, regional and global contexts based on the situation in early April 2020. They are given in the following:

• The pandemic situation has spread as a global pandemic disease which is creating fear, stress, stigma, minimising social networks, etc.
• Health and medical systems, especially biomedical systems, have taken their maximum effort but the healthcare system itself is affected due to various conditions in nature. Thus, there are many deaths reported though the biomedical system has made an enormous effort.
• High rate of deaths due to various illnesses or complications of diseases occurring amongst the patients, especially the elderly who are affected by COVID-19.
• Impossible tasks and challenges to the medical staff, supporting staff, social workers and health administrators at local, domestic and/or global levels. The World Health Organisation (WHO) is the primary entity followed by other United Nations (UN) agencies which are responsible during a regional and/or global pandemic situation.
• Some countries such as Italy, Spain, USA and China and a few other European countries are facing a much higher disintegration in all subsystems of society. Thus, the social system needs a complete reorganisation and integration to survive.
• Dismantling the family relationship and intimate relationships with relatives, neighbours, various communities, etc. These conditions may lead to interpersonal conflicts and domestic violence in the family.
• Losing the knowledge, experience, and services of the elderly would mean that the next generation would not be able to share them for their betterment.
• Downward trends of family economic conditions and several lower hierarchy social classes facing unbearable economic hardships due to lack of daily or monthly earnings. Though there are market accessibility and supply chain network even under the limitation of social mobility, they do not possess the purchasing power.
• Disruptions of schools, universities and vocational education segments where they have to seek certain optional strategies to cover up their educational goals. Especially, they may face some irreversible gaps in their education. All supply chain networks in the education field have been interrupted.
• Some people may face various kinds of stress, social stigma and depression conditions due to the malfunction of the social system.
• Possible social conflicts or conflicts of interests in the subsystems and this may lead towards the social system. Some institutions and organisations may not possess sufficient capacities to find remedial solutions to fill the gaps and issues.
• Decline in religious belief systems and practices in all religions and people may not believe in superstitious powers, in god and other divine and invisible elements in society.
• If the government and its subordinate authorities face some inadequate workable decisions or binding decisions and poor policy applications, it may lead to political instability in society. These conflicts of interests may cause political changes in society.
• Social unrest, stress and social stigma amongst the family members due to their detachment from family as a result of local and international migration. This may get aggravated further through the suspension of continental air transportation under lock-down situations.
• Disruptions in the productions of primary and secondary items in the society. Especially, issues in the production of primary items may lead to social unrest in society. Less demand and lack of proper supply chain networks may aggravate the existing unrest level.
• There are many people who are losing their jobs and incomes in the formal and informal sectors in the society.
• Service providers’ (supply chain networks) inability to continue the day-to-day supply of commodities and other services due to the lack of profit margins. It may lead to detachments from such entities or services, and some people may find alternative solutions. Thus, there may be some temporal decline in supply chains in society.
• People might depend on rumours and other informal channels of information if there is a chaotic condition in formation channels or the dissemination of information. Especially, if there are some loop halls in supply chain networks, these rumours may work rapidly with different social and economic impacts.
• Certain social classes may display their egoistic ideologies at the time of accumulating primary or essential goods and services. This may cause some negative attitudes amongst other social classes by creating conflict of interests.
• Global economic recession and increase in poverty level in society. This may lead to financial crises such as a decline in monetary values, share market values and businesses, changes in supply chain networks, and purchasing power of the people.
• The country has to take alternative action to maintain a stable economy. The developing countries and poor countries may get more loans and grants for their economy to survive. Thus, there will be more economic and political dependency in these countries. As a result of this condition, countries in the hegemonic circle may directly or indirectly fulfil their hidden agendas in the territories of such dependent countries. Being South Asian countries, there are certain common social and economic characteristics that need to be safeguarded during the pandemic situation.
• The pandemic situation will directly influence the Sustainable Development Goals (SDGs) defined to be achieved by 2030, since some countries may not allocate financial resources to meet the country-specific targets.
• Internal fragmentation may occur in global hegemonic countries, particularly in the USA and they may attempt to regain the hegemonic power through various economic and political strategies. Perhaps, they may go for direct war strategies with other countries or accelerate existing intervention in the Middle-East region.
• Sri Lankans who have been employed in foreign countries may return home and future foreign revenues may reduce. It is one of the major revenue in Sri Lanka, especially the migrant workers in the Middle-East, Europe and East Asian countries made a significant contribution to the Sri Lankan economy. This may cause some effects in supply chain networks in Sri Lanka as well as South Asian countries.
• If the government of Sri Lanka does not engage the returning migrants fruitfully, they may get frustrated and thus cause some vulnerability in the informal sectors of society.

When these positive and negative impacts are considered, it is very clear that COVID-19 has caused more positive impacts to the nations, regions and the world, particularly to South Asian countries. However, some countries such as Italy, Spain, the USA, and China and several European countries have suffered a lot. At the time of writing this article, COVID-19 has infected 5,306,928 persons worldwide (when the article was finalised for publication, the number has increased up to 15,947,291). Comparatively, these positive and negative impacts are valid for these countries too, irrespectively of the level of the spread of COVID-19.

The proposed future action plan or COVID-19 recovery action plan mainly focuses on the socio-economic, environment and political spheres and not on the medical and technical spheres. Table  1 illustrates this categorically.

Table 1

COVID-19 recovery action plan related to socio-economic, environment and political domains

X = good attention

XX = high attention

XXX = very high attention

When these different domains or spheres are looked at, it becomes very clear that there are higher numbers of actions identified under social domains. It implies that COVID-19 is a pandemic situation and it has more critical aspects in the social domain which needs to be looked at through a sociological rather than administrative or political perspective. Furthermore, all responsible officers as well as the public could provide at least a minimum input than all actions highlighted in Table  1 . Moreover, most of the proposed actions are focused on short-term and medium-term actions. However, there are some significant actions identified under long-term actions which are related to individual and group level attitudes.

All these analyses done and elaborated in different subsections of the paper highlight that there is a pivotal role to be played by the supply chain networks and management clusters in every society. In South Asian countries, the situation in supply chain networks and management is more specific than in other parts of the world because all these countries maintain a dual mode of economy—subsistence and commercial. Particularly, end users and consumers are engaged in some domestic or cottage production systems. If they have a surplus, then they share it with their relatives and neighbours as a cultural habit. Thus, they are not totally dependent on the market system. Some of them sell these domestic products to boutiques. Sometimes they give some value addition as cottage products and try to sustain a stable supply chain network. This situation is seen in many South Asian countries. Therefore, sustainability in supply chain networks in South Asian countries is highly volatile. However, it is obvious that the supply chain networks are influential and of paramount importance under the COVID 19 pandemic situation in South Asian countries.

Especially, if the authorities focus on this COVID 19 recovery action plan, it will help protect the family, environment and the nation. Therefore, we need to have an attitudinal change towards a more sustainable control of the pandemic situation in our country. The entire analysis of this paper is mainly based on the researcher’s Sri Lankan and other South Asian countries’ experience, particularly in development planning. The insight will help to manage and control the COVID-19 pandemic situation in South Asian countries. If we are able to adapt to the situation as explained, this approach will help us to maintain a stable and sustainable development programme for these countries, particularly for Sri Lanka.

When compared to several historical experiences in managing pandemic situations, this COVID-19 pandemic situation can be used as a development opportunity to enjoy a much higher take-off with a new set of attitudes in the process of development.

is attached to the Department of Sociology, University of Kelaniya, Sri Lanka. He is the Cadre Chair Professor in Sociology and he has vast experience in social impact assessment, social evaluations, social development and resettlement. He works as a freelance consultant for the Asian Development Bank, World Bank, UN Agencies and other similar organisations. He gained his doctorate from Centre for Study Social Systems, School of Social Sciences, Jawaharlal Nehru University, New Delhi in India.

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• The positive effects...

The positive effects of covid-19

Read our latest coverage of the coronavirus pandemic.

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• Bryn Nelson , science journalist
• Seattle, WA, USA
• bdnelson{at}nasw.org

As the coronavirus pandemic continues its deadly path, dramatic changes in how people live are reducing some instances of other medical problems. Bryn Nelson writes that the irony may hold valuable lessons for public health

Doctors and researchers are noticing some curious and unexpectedly positive side effects of the abrupt shifts in human behaviour in response to the covid-19 pandemic. Skies are bluer, fewer cars are crashing, crime is falling, and some other infectious diseases are fading from hospital emergency departments.

Other changes are unquestionably troubling. American doctors have expressed alarm over a nosedive in patients presenting to emergency departments with heart attacks, strokes, and other conditions, leading to fears that patients are too afraid of contracting covid-19 to seek necessary medical care. 1 Calls to poison control centres are up by around 20%, attributed to a rise in accidents with cleaners and disinfectants even before President Trump questioned whether injected disinfectants might stop the virus. 2 Calls to suicide prevention lines are skyrocketing, while health experts are fretting about signs of rising alcohol and drug use, poorer diets, and a lack of exercise among those cooped-up at home. 3 Millions of people are hungry and unemployed.

But doctors, researchers, and public health officials say the pandemic is also providing a unique window through which to view some positive health effects from major changes in human behaviour. And the pandemic may lead to a public more willing to accept and act on public health messages.

Alice Pong, a paediatric infectious disease physician and the medical director for infection control at Rady Children’s Hospital in San Diego, California, said the hospital has seen a sharp decline in paediatric admissions for respiratory illnesses. These include diseases such as influenza, parainfluenza, respiratory syncytial virus, and human metapneumovirus.

“We track positive viral tests through our hospital lab and those numbers have gone down dramatically since everybody went into quarantine,” Pong told The BMJ . “We do think that’s a reflection of kids not being in day care or school.” The hospital is testing fewer patients, she said, which could be because more children might be staying home with respiratory symptoms. But more serious cases and intensive care unit admissions are down as well, suggesting a true decline in life threatening illnesses.

Beyond the disease reducing effects of social distancing, Pong said she believes children and families are taking advice on hand washing, personal hygiene, and other prevention measures seriously. “I think this is going to be a good lesson for everybody,” she said. ‘‘The public is seeing why public health officials have advised them stay home when they feel sick, for example, and why they’ve emphasised hand washing and covering a cough or sneeze. Kids growing up now will know this is how germs are spread,” Pong said. That message could spread to their families and broaden awareness.

Fewer cars, blue skies

With covid-19 shutting down economic activity in most parts of the world and people staying closer to home, street crimes like assault and robbery are down significantly, though domestic violence has increased. 4 Traffic has plummeted as well. As a result, NASA satellites have documented significant reductions in air pollution—20-30% in many cases—in major cities around the world. 5 Based on those declines, Marshall Burke, an environmental economist at Stanford University, predicted in a blog post that two months’ worth of improved air quality in China alone might save the lives of 4000 children under the age of 5 and 73 000 adults over the age of 70 (a more conservative calculation estimated about 50 000 saved lives). 6

Although baseline pollution levels in the US are lower, Burke said a similar 20-30% reduction in pollution would still likely yield significant health benefits. “A pandemic is a terrible way to improve environmental health,” he emphasised. It may, however, provide an unexpected vantage to help understand how environmental health can be altered. “It may help bring into focus the effect of business as usual on health outcomes that we care about,” he told The BMJ . “In some sense, it helps us imagine the future.” Getting there, he says, could instead come through better regulation and technology.

A separate report coauthored by Fraser Shilling, director of the Road Ecology Center at the University of California at Davis, found that highway accidents—including those involving an injury or fatality—fell by half after the state’s shelter-in-place order on 19 March. 7 “The reduction in traffic accidents is unparalleled,” and yielded an estimated $40m (£32m; €37m) in public savings every day, the report asserted.

Whereas average traffic speeds increased by only a few miles per hour, traffic volume fell by 55%. Hospitals in the Sacramento region reported fewer trauma related admissions while other reports indicated fewer car collisions with pedestrians and cyclists.

In Washington, collisions on state highways fell even further—by 62%—in the month after the state’s stay-at-home order went into effect on 23 March, compared with the previous year, according to the Washington State Patrol. The question, Shilling said, is whether researchers can learn from the information to design safer transportation patterns. “We’re not going to be guessing anymore about what happens when you take half the cars away,” he said.

Emptier highways, though, may be triggering reckless driving that could undo the mortality reductions. Washington State Patrol spokesperson Darren Wright said that troopers are seeing a “scary trend” of more drivers travelling at extreme speeds—a phenomenon also observed in Missouri. “We’re seeing speeds in the 120 and 130 miles per hour range,” Wright said. One motorcyclist was clocked at more than 150 miles per hour.

Reassessing priorities

If the pandemic has prompted risky behaviour for some, it has encouraged others to embrace preventive measures. Randy Mayer, chief of the Bureau of HIV, STD, and Hepatitis at the Iowa Department of Public Health, said the public has become more responsive to calls from the department’s partner services, which perform contact tracing for people who test positive for HIV, gonorrhoea, and syphilis. “People are really interested in calling us back and finding out what information we have for them,” he said. That increased cooperation, Mayer said, may be a benefit of people associating public health departments with trying to keep them safe from covid-19.

Even so, he worries that a noticeable reduction in the number of new HIV diagnoses may partially reflect a reduction in available testing with many clinics open for limited hours, if not completely closed. But growing evidence suggests that more people are also heeding recent pleas by public health officials and even dating apps to reduce the risk of covid-19 infection by avoiding casual sex with new partners. Researchers in Portugal and the UK told The BMJ that they were beginning to see shifts in the incidence of sexually transmitted infections but were still collecting data to support their observations.

Miguel Duarte Botas Alpalhão, a dermatovenereologist and invited lecturer in the Faculty of Medicine at the University of Lisbon, said that he expects to see a lower rate of sexually transmitted infections during the lockdown. The crisis has caused people to question their priorities “and how much they are willing to give up to protect their lives and those of their loved ones,” he said. “People are now more aware that nothing really matters when health is lacking, and this raised awareness may be the driving force towards healthier habits. We will have to wait and see.”

Competing interests: I have read and understood BMJ policy on declaration of interests and have no relevant interests to declare.

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Impact of campus closure during COVID-19 on lifestyle, educational performance, and anxiety levels of college students in China

Affiliations.

• 1 Hebei Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, 050024, China.
• 2 Hebei Collaborative Innovation Center for Eco-Environment, Hebei Normal University, Shijiazhuang, 050024, China.
• 3 College of Education, Hebei Normal University, Shijiazhuang, 050024, China.
• 4 School of Languages and Culture, Hebei GEO University, Shijiazhuang, 050031, China.
• 5 School of Languages and Culture, Hebei GEO University, Shijiazhuang, 050031, China. [email protected].
• 6 Hebei Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, 050024, China. [email protected].
• 7 Hebei Collaborative Innovation Center for Eco-Environment, Hebei Normal University, Shijiazhuang, 050024, China. [email protected].
• PMID: 39148106
• PMCID: PMC11325596
• DOI: 10.1186/s12889-024-19744-8

Background: Higher education students exhibit heightened sensitivity to environmental changes as they navigate the critical transition from adolescence to adulthood. The coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented challenges to universities worldwide, exemplifying a crisis that profoundly affects the learning outcomes and psychological status of college students. Although it is known that campus lockdown has triggered dramatic changes in lifestyles, learning outcomes, and psychological statuses, in-depth knowledge of the causal relationships among these changes remains largely unclear.

Methods: Here, we conducted a cross-sectional survey designed to assess the impact of campus closure during COVID-19 on lifestyle, educational performance, and anxiety levels among college students. We surveyed over 3,500 junior college, undergraduate and graduate participants from 94 colleges/universities across 30 provinces, municipalities, and autonomous regions in China.We employed structural equation modeling (SEM) to explore the relationships between changes in lifestyle, educational performance, and levels of anxiety associated with campus open or closure regulations during the COVID-19 pandemic.

Results: Our results discovered that sleep duration, physical activity involvement, and social connections were crucial for sustaining students' learning outcomes and mental health. The shift to online learning and campus lockdown exacerbated stressors, contributing to heightened anxiety (β = 0.066), disrupted sleep patterns, and enhanced physical activity (β = 0.070) and reduced learning effect (β = -0.059). Sleep patterns were disrupted by the campus lockdown, an effect mediated by the degradation of relationships among classmates. Nonetheless, the best-fitting SEM uncovered the intricate relationships among lifestyle changes, learning outcomes, and psychological status in response to sudden environmental changes (Fisher's C = 80.949, P = 0.328). These results highlight the critical role of adaptable, supportive campus policies tailored to meet the diverse needs and interests of students during and beyond crises (Fisher's C = 59.568, P = 0.809).

Conclusions: Our study advocates for a holistic approach that addresses the multifaceted aspects of student life to cultivate a resilient academic community. This approach contributes to a deeper understanding of the effects of sudden environmental changes on students' psychological well-being and academic performance in the post-pandemic era.

Keywords: COVID-19 pandemic; Campus lockdown; College student; Lifestyle; Psychological status.

© 2024. The Author(s).

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Conflict of interest statement

The authors declare no competing interests.

The flowchart of this study…

The flowchart of this study outlines the methodology. The online survey was conducted…

The demographics, campus life, opinions…

The demographics, campus life, opinions on campus lockdown, and anxiety levels were examined…

A structural equation modeling for…

A structural equation modeling for the relationships among various factors affecting the academic…

A structural equation modeling for the relationships among various factors affecting the opinions…

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When Do Ordinal Ability Rank Effects Emerge? Evidence from the Timing of School Closures

IZA Discussion Paper No. 17222

13 Pages Posted: 19 Aug 2024

Marco Bertoni

University of Padua

Saeideh Parkam

University of Naples Federico II

We leverage the timing of pandemic-induced school closures to learn about the emergence of ordinal rank effects in education. Using administrative data from Italian middle schools for four cohorts of students, our study reveals that disrupting peer interactions during the first year of middle school - when students are still unfamiliar with one another - substantially diminishes the impact of ordinal rank on test scores. Instead, later interruptions to peer interactions do not significantly affect the strength of these interpersonal comparisons.

Keywords: ability peer effect, ordinal ability rank, school closures, COVID-19

JEL Classification: I21, I24, J24

Suggested Citation: Suggested Citation

Marco Bertoni (Contact Author)

University of padua ( email ), university of naples federico ii ( email ).

Naples Italy

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• DOI: 10.55041/ijsrem32045
• Corpus ID: 269455254

IMPACT OF SOCIAL MEDIA ON STUDENTS

• Raj Ranjan Jha
• Published in INTERANTIONAL JOURNAL OF… 28 April 2024
• Education, Computer Science

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