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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).

covid 19 essay for students

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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Writing about COVID-19 in a college admission essay

by: Venkates Swaminathan | Updated: September 14, 2020

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Writing about COVID-19 in your college admission essay

For students applying to college using the CommonApp, there are several different places where students and counselors can address the pandemic’s impact. The different sections have differing goals. You must understand how to use each section for its appropriate use.

The CommonApp COVID-19 question

First, the CommonApp this year has an additional question specifically about COVID-19 :

Community disruptions such as COVID-19 and natural disasters can have deep and long-lasting impacts. If you need it, this space is yours to describe those impacts. Colleges care about the effects on your health and well-being, safety, family circumstances, future plans, and education, including access to reliable technology and quiet study spaces. Please use this space to describe how these events have impacted you.

This question seeks to understand the adversity that students may have had to face due to the pandemic, the move to online education, or the shelter-in-place rules. You don’t have to answer this question if the impact on you wasn’t particularly severe. Some examples of things students should discuss include:

  • The student or a family member had COVID-19 or suffered other illnesses due to confinement during the pandemic.
  • The candidate had to deal with personal or family issues, such as abusive living situations or other safety concerns
  • The student suffered from a lack of internet access and other online learning challenges.
  • Students who dealt with problems registering for or taking standardized tests and AP exams.

Jeff Schiffman of the Tulane University admissions office has a blog about this section. He recommends students ask themselves several questions as they go about answering this section:

  • Are my experiences different from others’?
  • Are there noticeable changes on my transcript?
  • Am I aware of my privilege?
  • Am I specific? Am I explaining rather than complaining?
  • Is this information being included elsewhere on my application?

If you do answer this section, be brief and to-the-point.

Counselor recommendations and school profiles

Second, counselors will, in their counselor forms and school profiles on the CommonApp, address how the school handled the pandemic and how it might have affected students, specifically as it relates to:

  • Grading scales and policies
  • Graduation requirements
  • Instructional methods
  • Schedules and course offerings
  • Testing requirements
  • Your academic calendar
  • Other extenuating circumstances

Students don’t have to mention these matters in their application unless something unusual happened.

Writing about COVID-19 in your main essay

Write about your experiences during the pandemic in your main college essay if your experience is personal, relevant, and the most important thing to discuss in your college admission essay. That you had to stay home and study online isn’t sufficient, as millions of other students faced the same situation. But sometimes, it can be appropriate and helpful to write about something related to the pandemic in your essay. For example:

  • One student developed a website for a local comic book store. The store might not have survived without the ability for people to order comic books online. The student had a long-standing relationship with the store, and it was an institution that created a community for students who otherwise felt left out.
  • One student started a YouTube channel to help other students with academic subjects he was very familiar with and began tutoring others.
  • Some students used their extra time that was the result of the stay-at-home orders to take online courses pursuing topics they are genuinely interested in or developing new interests, like a foreign language or music.

Experiences like this can be good topics for the CommonApp essay as long as they reflect something genuinely important about the student. For many students whose lives have been shaped by this pandemic, it can be a critical part of their college application.

Want more? Read 6 ways to improve a college essay , What the &%$! should I write about in my college essay , and Just how important is a college admissions essay? .

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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.
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How to Write About the Impact of the Coronavirus in a College Essay

The global impact of COVID-19, the disease caused by the novel coronavirus, means colleges and prospective students alike are in for an admissions cycle like no other. Both face unprecedented challenges and questions as they grapple with their respective futures amid the ongoing fallout of the pandemic.

Colleges must examine applicants without the aid of standardized test scores for many -- a factor that prompted many schools to go test-optional for now . Even grades, a significant component of a college application, may be hard to interpret with some high schools adopting pass-fail classes last spring due to the pandemic. Major college admissions factors are suddenly skewed.

"I can't help but think other (admissions) factors are going to matter more," says Ethan Sawyer, founder of the College Essay Guy, a website that offers free and paid essay-writing resources.

College essays and letters of recommendation , Sawyer says, are likely to carry more weight than ever in this admissions cycle. And many essays will likely focus on how the pandemic shaped students' lives throughout an often tumultuous 2020.

[ Read: How to Write a College Essay. ]

But before writing a college essay focused on the coronavirus, students should explore whether it's the best topic for them.

Writing About COVID-19 for a College Application

Much of daily life has been colored by the coronavirus. Virtual learning is the norm at many colleges and high schools, many extracurriculars have vanished and social lives have stalled for students complying with measures to stop the spread of COVID-19.

"For some young people, the pandemic took away what they envisioned as their senior year," says Robert Alexander, dean of admissions, financial aid and enrollment management at the University of Rochester in New York. "Maybe that's a spot on a varsity athletic team or the lead role in the fall play. And it's OK for them to mourn what should have been and what they feel like they lost, but more important is how are they making the most of the opportunities they do have?"

That question, Alexander says, is what colleges want answered if students choose to address COVID-19 in their college essay.

But the question of whether a student should write about the coronavirus is tricky. The answer depends largely on the student.

"In general, I don't think students should write about COVID-19 in their main personal statement for their application," Robin Miller, master college admissions counselor at IvyWise, a college counseling company, wrote in an email.

"Certainly, there may be exceptions to this based on a student's individual experience, but since the personal essay is the main place in the application where the student can really allow their voice to be heard and share insight into who they are as an individual, there are likely many other topics they can choose to write about that are more distinctive and unique than COVID-19," Miller says.

[ Read: What Colleges Look for: 6 Ways to Stand Out. ]

Opinions among admissions experts vary on whether to write about the likely popular topic of the pandemic.

"If your essay communicates something positive, unique, and compelling about you in an interesting and eloquent way, go for it," Carolyn Pippen, principal college admissions counselor at IvyWise, wrote in an email. She adds that students shouldn't be dissuaded from writing about a topic merely because it's common, noting that "topics are bound to repeat, no matter how hard we try to avoid it."

Above all, she urges honesty.

"If your experience within the context of the pandemic has been truly unique, then write about that experience, and the standing out will take care of itself," Pippen says. "If your experience has been generally the same as most other students in your context, then trying to find a unique angle can easily cross the line into exploiting a tragedy, or at least appearing as though you have."

But focusing entirely on the pandemic can limit a student to a single story and narrow who they are in an application, Sawyer says. "There are so many wonderful possibilities for what you can say about yourself outside of your experience within the pandemic."

He notes that passions, strengths, career interests and personal identity are among the multitude of essay topic options available to applicants and encourages them to probe their values to help determine the topic that matters most to them -- and write about it.

That doesn't mean the pandemic experience has to be ignored if applicants feel the need to write about it.

Writing About Coronavirus in Main and Supplemental Essays

Students can choose to write a full-length college essay on the coronavirus or summarize their experience in a shorter form.

To help students explain how the pandemic affected them, The Common App has added an optional section to address this topic. Applicants have 250 words to describe their pandemic experience and the personal and academic impact of COVID-19.

[ Read: The Common App: Everything You Need to Know. ]

"That's not a trick question, and there's no right or wrong answer," Alexander says. Colleges want to know, he adds, how students navigated the pandemic, how they prioritized their time, what responsibilities they took on and what they learned along the way.

If students can distill all of the above information into 250 words, there's likely no need to write about it in a full-length college essay, experts say. And applicants whose lives were not heavily altered by the pandemic may even choose to skip the optional COVID-19 question.

"This space is best used to discuss hardship and/or significant challenges that the student and/or the student's family experienced as a result of COVID-19 and how they have responded to those difficulties," Miller notes. Using the section to acknowledge a lack of impact, she adds, "could be perceived as trite and lacking insight, despite the good intentions of the applicant."

To guard against this lack of awareness, Sawyer encourages students to tap someone they trust to review their writing , whether it's the 250-word Common App response or the full-length essay.

Experts tend to agree that the short-form approach to this as an essay topic works better, but there are exceptions. And if a student does have a coronavirus story that he or she feels must be told, Alexander encourages the writer to be authentic in the essay.

"My advice for an essay about COVID-19 is the same as my advice about an essay for any topic -- and that is, don't write what you think we want to read or hear," Alexander says. "Write what really changed you and that story that now is yours and yours alone to tell."

Sawyer urges students to ask themselves, "What's the sentence that only I can write?" He also encourages students to remember that the pandemic is only a chapter of their lives and not the whole book.

Miller, who cautions against writing a full-length essay on the coronavirus, says that if students choose to do so they should have a conversation with their high school counselor about whether that's the right move. And if students choose to proceed with COVID-19 as a topic, she says they need to be clear, detailed and insightful about what they learned and how they adapted along the way.

"Approaching the essay in this manner will provide important balance while demonstrating personal growth and vulnerability," Miller says.

Pippen encourages students to remember that they are in an unprecedented time for college admissions.

"It is important to keep in mind with all of these (admission) factors that no colleges have ever had to consider them this way in the selection process, if at all," Pippen says. "They have had very little time to calibrate their evaluations of different application components within their offices, let alone across institutions. This means that colleges will all be handling the admissions process a little bit differently, and their approaches may even evolve over the course of the admissions cycle."

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Anastasiya Kandratsenka George Washington High School, Class of 2021

At this point in time there shouldn't be a single person who doesn't know about the coronavirus, or as they call it, COVID-19. The coronavirus is a virus that originated in China, reached the U.S. and eventually spread all over the world by January of 2020. The common symptoms of the virus include shortness of breath, chills, sore throat, headache, loss of taste and smell, runny nose, vomiting and nausea. As it has been established, it might take up to 14 days for the symptoms to show. On top of that, the virus is also highly contagious putting all age groups at risk. The elderly and individuals with chronic diseases such as pneumonia or heart disease are in the top risk as the virus attacks the immune system. 

The virus first appeared on the news and media platforms in the month of January of this year. The United States and many other countries all over the globe saw no reason to panic as it seemed that the virus presented no possible threat. Throughout the next upcoming months, the virus began to spread very quickly, alerting health officials not only in the U.S., but all over the world. As people started digging into the origin of the virus, it became clear that it originated in China. Based on everything scientists have looked at, the virus came from a bat that later infected other animals, making it way to humans. As it goes for the United States, the numbers started rising quickly, resulting in the cancellation of sports events, concerts, large gatherings and then later on schools. 

As it goes personally for me, my school was shut down on March 13th. The original plan was to put us on a two weeks leave, returning on March 30th but, as the virus spread rapidly and things began escalating out of control very quickly, President Trump announced a state of emergency and the whole country was put on quarantine until April 30th. At that point, schools were officially shut down for the rest of the school year. Distanced learning was introduced, online classes were established, a new norm was put in place. As for the School District of Philadelphia distanced learning and online classes began on May 4th. From that point on I would have classes four times a week, from 8AM till 3PM. Virtual learning was something that I never had to experience and encounter before. It was all new and different for me, just as it was for millions of students all over the United States. We were forced to transfer from physically attending school, interacting with our peers and teachers, participating in fun school events and just being in a classroom setting, to just looking at each other through a computer screen in a number of days. That is something that we all could have never seen coming, it was all so sudden and new. 

My experience with distanced learning was not very great. I get distracted very easily and   find it hard to concentrate, especially when it comes to school. In a classroom I was able to give my full attention to what was being taught, I was all there. However, when we had the online classes, I could not focus and listen to what my teachers were trying to get across. I got distracted very easily, missing out on important information that was being presented. My entire family which consists of five members, were all home during the quarantine. I have two little siblings who are very loud and demanding, so I’m sure it can be imagined how hard it was for me to concentrate on school and do what was asked of me when I had these two running around the house. On top of school, I also had to find a job and work 35 hours a week to support my family during the pandemic. My mother lost her job for the time being and my father was only able to work from home. As we have a big family, the income of my father was not enough. I made it my duty to help out and support our family as much as I could: I got a job at a local supermarket and worked there as a cashier for over two months. 

While I worked at the supermarket, I was exposed to dozens of people every day and with all the protection that was implemented to protect the customers and the workers, I was lucky enough to not get the virus. As I say that, my grandparents who do not even live in the U.S. were not so lucky. They got the virus and spent over a month isolated, in a hospital bed, with no one by their side. Our only way of communicating was through the phone and if lucky, we got to talk once a week. Speaking for my family, that was the worst and scariest part of the whole situation. Luckily for us, they were both able to recover completely. 

As the pandemic is somewhat under control, the spread of the virus has slowed down. We’re now living in the new norm. We no longer view things the same, the way we did before. Large gatherings and activities that require large groups to come together are now unimaginable! Distanced learning is what we know, not to mention the importance of social distancing and having to wear masks anywhere and everywhere we go. This is the new norm now and who knows when and if ever we’ll be able go back to what we knew before. This whole experience has made me realize that we, as humans, tend to take things for granted and don’t value what we have until it is taken away from us. 

Articles in this Volume

[tid]: dedication, [tid]: new tools for a new house: transformations for justice and peace in and beyond covid-19, [tid]: black lives matter, intersectionality, and lgbtq rights now, [tid]: the voice of asian american youth: what goes untold, [tid]: beyond words: reimagining education through art and activism, [tid]: voice(s) of a black man, [tid]: embodied learning and community resilience, [tid]: re-imagining professional learning in a time of social isolation: storytelling as a tool for healing and professional growth, [tid]: reckoning: what does it mean to look forward and back together as critical educators, [tid]: leader to leaders: an indigenous school leader’s advice through storytelling about grief and covid-19, [tid]: finding hope, healing and liberation beyond covid-19 within a context of captivity and carcerality, [tid]: flux leadership: leading for justice and peace in & beyond covid-19, [tid]: flux leadership: insights from the (virtual) field, [tid]: hard pivot: compulsory crisis leadership emerges from a space of doubt, [tid]: and how are the children, [tid]: real talk: teaching and leading while bipoc, [tid]: systems of emotional support for educators in crisis, [tid]: listening leadership: the student voices project, [tid]: global engagement, perspective-sharing, & future-seeing in & beyond a global crisis, [tid]: teaching and leadership during covid-19: lessons from lived experiences, [tid]: crisis leadership in independent schools - styles & literacies, [tid]: rituals, routines and relationships: high school athletes and coaches in flux, [tid]: superintendent back-to-school welcome 2020, [tid]: mitigating summer learning loss in philadelphia during covid-19: humble attempts from the field, [tid]: untitled, [tid]: the revolution will not be on linkedin: student activism and neoliberalism, [tid]: why radical self-care cannot wait: strategies for black women leaders now, [tid]: from emergency response to critical transformation: online learning in a time of flux, [tid]: illness methodology for and beyond the covid era, [tid]: surviving black girl magic, the work, and the dissertation, [tid]: cancelled: the old student experience, [tid]: lessons from liberia: integrating theatre for development and youth development in uncertain times, [tid]: designing a more accessible future: learning from covid-19, [tid]: the construct of standards-based education, [tid]: teachers leading teachers to prepare for back to school during covid, [tid]: using empathy to cross the sea of humanity, [tid]: (un)doing college, community, and relationships in the time of coronavirus, [tid]: have we learned nothing, [tid]: choosing growth amidst chaos, [tid]: living freire in pandemic….participatory action research and democratizing knowledge at knowledgedemocracy.org, [tid]: philly students speak: voices of learning in pandemics, [tid]: the power of will: a letter to my descendant, [tid]: photo essays with students, [tid]: unity during a global pandemic: how the fight for racial justice made us unite against two diseases, [tid]: educational changes caused by the pandemic and other related social issues, [tid]: online learning during difficult times, [tid]: fighting crisis: a student perspective, [tid]: the destruction of soil rooted with culture, [tid]: a demand for change, [tid]: education through experience in and beyond the pandemics, [tid]: the pandemic diaries, [tid]: all for one and 4 for $4, [tid]: tiktok activism, [tid]: why digital learning may be the best option for next year, [tid]: my 2020 teen experience, [tid]: living between two pandemics, [tid]: journaling during isolation: the gold standard of coronavirus, [tid]: sailing through uncertainty, [tid]: what i wish my teachers knew, [tid]: youthing in pandemic while black, [tid]: the pain inflicted by indifference, [tid]: education during the pandemic, [tid]: the good, the bad, and the year 2020, [tid]: racism fueled pandemic, [tid]: coronavirus: my experience during the pandemic, [tid]: the desensitization of a doomed generation, [tid]: a philadelphia war-zone, [tid]: the attack of the covid monster, [tid]: back-to-school: covid-19 edition, [tid]: the unexpected war, [tid]: learning outside of the classroom, [tid]: why we should learn about college financial aid in school: a student perspective, [tid]: flying the plane as we go: building the future through a haze, [tid]: my covid experience in the age of technology, [tid]: we, i, and they, [tid]: learning your a, b, cs during a pandemic, [tid]: quarantine: a musical, [tid]: what it’s like being a high school student in 2020, [tid]: everything happens for a reason, [tid]: blacks live matter – a sobering and empowering reality among my peers, [tid]: the mental health of a junior during covid-19 outbreaks, [tid]: a year of change, [tid]: covid-19 and school, [tid]: the virtues and vices of virtual learning, [tid]: college decisions and the year 2020: a virtual rollercoaster, [tid]: quarantine thoughts, [tid]: quarantine through generation z, [tid]: attending online school during a pandemic.

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The impact of COVID-19 on student achievement and what it may mean for educators

Subscribe to the brown center on education policy newsletter, jim soland , jim soland assistant professor, school of education and human development - university of virginia, affiliated research fellow - nwea @jsoland megan kuhfeld , megan kuhfeld senior research scientist - nwea @megankuhfeld beth tarasawa , bt beth tarasawa executive vice president of research - nwea @bethtarasawa angela johnson , aj angela johnson research scientist - nwea erik ruzek , and er erik ruzek research assistant professor, curry school of education - university of virginia jing liu jing liu assistant professor of education policy - university of maryland-college park @drjingliu.

May 27, 2020

This Chalkboard post from May 2020 draws on historical data and past research to forecast the possible impact of COVID-19 school closures on student achievement. With actual data from the 2020-21 school year now available, please see this December 2020 Chalkboard post for an updated analysis of this trend.

Virtually all K-12 students in the United States are currently missing face-to-face instruction due to COVID-19. Many parents and educators thus share a common worry: When the pandemic subsides, kids will return to school with lower achievement. There are also concerns that the gap between high- and low-achieving students will become larger. Given the need to address these concerns, we decided to use prior test scores from millions of students and leverage research on summer learning patterns to make informed projections of what learning loss due to the pandemic might look like. Ultimately, we wanted to know: What sort of learning losses could we expect from the shortened 2019-20 school year?

Answering this question is complicated by the unique circumstances of COVID-19. Current school closures have added to the time that most students already spend at home during the summer months without explicit face-to-face instruction from teachers. Meanwhile, teachers are scrambling to adapt content for an online platform and parents are juggling work responsibilities (if not joblessness) with caring for and educating their own children. Students themselves are faced with isolation, anxiety about a deadly virus, and uncertainty about the future. In so many ways, the current situation is unprecedented for most people alive today.

Yet there are parallels between the current situation and other reasons students miss school that can give us insight into how COVID-19 may affect achievement. This includes research on the effects of out-of-school time on learning due to absenteeism , weather-related school closures (e.g., Hurricane Katrina in New Orleans), and summer vacation . Existing evidence can provide a rough sense of how time out of school due to COVID-19 will affect achievement.

We relied heavily on past precedent when trying to understand how COVID-19 might impact achievement in the short and medium term. We used a national sample of over 5 million students in grades 3-8 who took MAP Growth assessments in 2017-2018. These assessments enable such estimates because MAP Growth is administered multiple times per year, which means test scores are available in fall, winter, and spring such that changes in achievement during the year can be understood and anticipated. We compared typical growth for students who completed a standard-length school year to projections under multiple scenarios. These scenarios were directly informed by out-of-school-time research.

The results are deeply concerning.

The two figures below show projected math and reading learning patterns from the beginning of the 2019-20 school year (before COVID-19 school closures) through the start of the 2020-21 school year. The solid lines represent average trajectories in a typical year with typical growth (estimated based on a prior year’s data) followed by normal patterns of learning loss over the summer (generally, student achievement/learning tends to decline during the summer, though this varies greatly by student). Next, we assume an extended summer loss would occur during the period since schools closed. We refer to this scenario as the “COVID Slide” (represented by the dotted lines). These projections give a sense of how much learning students could lose, though we hope they will be overestimations of loss, given the online instruction and home schooling occurring.

F1 COVID-19 learning loss - mathematics forecast

These preliminary COVID Slide estimates suggest students could begin fall 2020 with roughly 70% of the learning gains in reading from the prior year relative to a typical school year. In mathematics, students may show even smaller learning gains from the previous year, returning with less than 50% of the gains. In lower grades, students may be nearly a full year behind in math compared to what we would observe in normal conditions.

Though not shown in the figures, we produced similar estimates of learning loss based on research showing the effect of being absent on achievement. That is, we simply assumed students’ learning during COVID-19 school closures would be akin to what occurs when students miss school, a large assumption given the online learning and homeschooling now occurring. Results for absenteeism-based projections were often more dire.

We also examined how much more variable achievement might be in the fall—that is, how wide the range in achievement might be between very high and very low-performing students. This range has implications for whether teachers can provide similar content to all students in their classrooms, or if they might need to further differentiate instruction based on a broader range of needs.

f3 Learning loss in 4th and 6th grade in mathematics

The above figures show our estimate of that variability by subject for 4 th and 6 th grade. The shaded areas display the spread in potential outcomes between students who were in the 25 th percentile of summer learning loss (who showed steep declines) and those in the 75 th percentile (who showed flat lines or even small gains during the summer). In mathematics, we see a fair amount of variability in learning rates, though the majority of students show losses over the extended closure and summer period. However, in reading, there is an even wider spread of potential outcomes, with students who are in the 75 th percentile and above showing sizable learning gains during the summer. Further, the figure below shows that extended time out of school may lead to more variability in achievement when students return in the fall relative to a typical year. A wider range of learning needs like the ones suggested by the figure could create greater challenges for teachers.

f5 math and reading

The New York Times warns that today’s students could be the “COVID generation.” As we think through our road to recovery, we hope education leaders consider our projections among many data points when preparing to support students returning in the fall. Specifically, our results indicate that:

  • Students may be substantially behind, especially in mathematics . Thus, teachers of different grade levels may wish to coordinate in order to determine where to start instruction. Educators will also need to find ways to assess students early, either formally or informally, to understand exactly where students are academically.
  • Students are likely to enter school with more variability in their academic skills than under normal circumstances. Therefore, educators may need to consider ways to further differentiate instruction or provide opportunities for individualized learning.
  • Students who lose the most during the summer tend to gain the most when back in school, but this may not hold for COVID-19 . Regardless, the ground that students have to make up during the 2020-21 academic year will probably be greater due to COVID-19. Therefore, educators may want to work with students to determine growth rates needed to catch up and set learning goals for the year that are ambitious but obtainable.

Finally, the effects of COVID-19 our study cannot examine may be the ones most worthy of addressing. Prior research on students displaced by Hurricane Katrina indicated that they had difficulty concentrating and often manifested symptoms of depression in the months following the hurricane. Understanding these impacts and how best to support students’ social and emotional needs after the huge disruption of COVID-19 will be essential. Many students may face greater food insecurity, loss of family income, loss of family members to the coronavirus, and fear of catching the virus themselves.

While the scale of the COVID-19 school closures is novel, the inequalities in our school systems are unfortunately anything but new. Our models cannot account for the reality that the crisis is having an unequal impact on our most underserved communities. Nonetheless, we hope these analyses, which synthesize what we know from existing bodies of research, will inform tomorrow’s decision-making.

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Coronavirus: The world has come together to flatten the curve. Can we stay united to tackle other crises?

Watching the world come together gives me hope for the future, writes mira patel, a high school junior..

Mira Patel and her sister Veda. (Courtesy of Dee Patel)

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Before the pandemic, I had often heard adults say that young people would lose the ability to connect in-person with others due to our growing dependence on technology and social media. However, this stay-at-home experience has proven to me that our elders’ worry is unnecessary. Because isolation isn’t in human nature, and no advancement in technology could replace our need to meet in person, especially when it comes to learning.

As the weather gets warmer and we approach summertime, it’s going to be more and more tempting for us teenagers to go out and do what we have always done: hang out and have fun. Even though the decision-makers are adults, everyone has a role to play and we teens can help the world move forward by continuing to self-isolate. It’s incredibly important that in the coming weeks, we respect the government’s effort to contain the spread of the coronavirus.

In the meantime, we can find creative ways to stay connected and continue to do what we love. Personally, I see many 6-feet-apart bike rides and Zoom calls in my future.

If there is anything that this pandemic has made me realize, it’s how connected we all are. At first, the infamous coronavirus seemed to be a problem in China, which is worlds away. But slowly, it steadily made its way through various countries in Europe, and inevitably reached us in America. What was once framed as a foreign virus has now hit home.

Watching the global community come together, gives me hope, as a teenager, that in the future we can use this cooperation to combat climate change and other catastrophes.

As COVID-19 continues to creep its way into each of our communities and impact the way we live and communicate, I find solace in the fact that we face what comes next together, as humanity.

When the day comes that my generation is responsible for dealing with another crisis, I hope we can use this experience to remind us that moving forward requires a joint effort.

Mira Patel is a junior at Strath Haven High School and is an education intern at the Foreign Policy Research Institute in Philadelphia. Follow her on Instagram here.  

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  • Published: 27 September 2021

Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap

  • Sébastien Goudeau   ORCID: orcid.org/0000-0001-7293-0977 1 ,
  • Camille Sanrey   ORCID: orcid.org/0000-0003-3158-1306 1 ,
  • Arnaud Stanczak   ORCID: orcid.org/0000-0002-2596-1516 2 ,
  • Antony Manstead   ORCID: orcid.org/0000-0001-7540-2096 3 &
  • Céline Darnon   ORCID: orcid.org/0000-0003-2613-689X 2  

Nature Human Behaviour volume  5 ,  pages 1273–1281 ( 2021 ) Cite this article

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The COVID-19 pandemic has forced teachers and parents to quickly adapt to a new educational context: distance learning. Teachers developed online academic material while parents taught the exercises and lessons provided by teachers to their children at home. Considering that the use of digital tools in education has dramatically increased during this crisis, and it is set to continue, there is a pressing need to understand the impact of distance learning. Taking a multidisciplinary view, we argue that by making the learning process rely more than ever on families, rather than on teachers, and by getting students to work predominantly via digital resources, school closures exacerbate social class academic disparities. To address this burning issue, we propose an agenda for future research and outline recommendations to help parents, teachers and policymakers to limit the impact of the lockdown on social-class-based academic inequality.

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The widespread effects of the COVID-19 pandemic that emerged in 2019–2020 have drastically increased health, social and economic inequalities 1 , 2 . For more than 900 million learners around the world, the pandemic led to the closure of schools and universities 3 . This exceptional situation forced teachers, parents and students to quickly adapt to a new educational context: distance learning. Teachers had to develop online academic materials that could be used at home to ensure educational continuity while ensuring the necessary physical distancing. Primary and secondary school students suddenly had to work with various kinds of support, which were usually provided online by their teachers. For college students, lockdown often entailed returning to their hometowns while staying connected with their teachers and classmates via video conferences, email and other digital tools. Despite the best efforts of educational institutions, parents and teachers to keep all children and students engaged in learning activities, ensuring educational continuity during school closure—something that is difficult for everyone—may pose unique material and psychological challenges for working-class families and students.

Not only did the pandemic lead to the closure of schools in many countries, often for several weeks, it also accelerated the digitalization of education and amplified the role of parental involvement in supporting the schoolwork of their children. Thus, beyond the specific circumstances of the COVID-19 lockdown, we believe that studying the effects of the pandemic on academic inequalities provides a way to more broadly examine the consequences of school closure and related effects (for example, digitalization of education) on social class inequalities. Indeed, bearing in mind that (1) the risk of further pandemics is higher than ever (that is, we are in a ‘pandemic era’ 4 , 5 ) and (2) beyond pandemics, the use of digital tools in education (and therefore the influence of parental involvement) has dramatically increased during this crisis, and is set to continue, there is a pressing need for an integrative and comprehensive model that examines the consequences of distance learning. Here, we propose such an integrative model that helps us to understand the extent to which the school closures associated with the pandemic amplify economic, digital and cultural divides that in turn affect the psychological functioning of parents, students and teachers in a way that amplifies academic inequalities. Bringing together research in social sciences, ranging from economics and sociology to social, cultural, cognitive and educational psychology, we argue that by getting students to work predominantly via digital resources rather than direct interactions with their teachers, and by making the learning process rely more than ever on families rather than teachers, school closures exacerbate social class academic disparities.

First, we review research showing that social class is associated with unequal access to digital tools, unequal familiarity with digital skills and unequal uses of such tools for learning purposes 6 , 7 . We then review research documenting how unequal familiarity with school culture, knowledge and skills can also contribute to the accentuation of academic inequalities 8 , 9 . Next, we present the results of surveys conducted during the 2020 lockdown showing that the quality and quantity of pedagogical support received from schools varied according to the social class of families (for examples, see refs. 10 , 11 , 12 ). We then argue that these digital, cultural and structural divides represent barriers to the ability of parents to provide appropriate support for children during distance learning (Fig. 1 ). These divides also alter the levels of self-efficacy of parents and children, thereby affecting their engagement in learning activities 13 , 14 . In the final section, we review preliminary evidence for the hypothesis that distance learning widens the social class achievement gap and we propose an agenda for future research. In addition, we outline recommendations that should help parents, teachers and policymakers to use social science research to limit the impact of school closure and distance learning on the social class achievement gap.

figure 1

Economic, structural, digital and cultural divides influence the psychological functioning of parents and students in a way that amplify inequalities.

The digital divide

Unequal access to digital resources.

Although the use of digital technologies is almost ubiquitous in developed nations, there is a digital divide such that some people are more likely than others to be numerically excluded 15 (Fig. 1 ). Social class is a strong predictor of digital disparities, including the quality of hardware, software and Internet access 16 , 17 , 18 . For example, in 2019, in France, around 1 in 5 working-class families did not have personal access to the Internet compared with less than 1 in 20 of the most privileged families 19 . Similarly, in 2020, in the United Kingdom, 20% of children who were eligible for free school meals did not have access to a computer at home compared with 7% of other children 20 . In 2021, in the United States, 41% of working-class families do not own a laptop or desktop computer and 43% do not have broadband compared with 8% and 7%, respectively, of upper/middle-class Americans 21 . A similar digital gap is also evident between lower-income and higher-income countries 22 .

Second, simply having access to a computer and an Internet connection does not ensure effective distance learning. For example, many of the educational resources sent by teachers need to be printed, thereby requiring access to printers. Moreover, distance learning is more difficult in households with only one shared computer compared with those where each family member has their own 23 . Furthermore, upper/middle-class families are more likely to be able to guarantee a suitable workspace for each child than their working-class counterparts 24 .

In the context of school closures, such disparities are likely to have important consequences for educational continuity. In line with this idea, a survey of approximately 4,000 parents in the United Kingdom confirmed that during lockdown, more than half of primary school children from the poorest families did not have access to their own study space and were less well equipped for distance learning than higher-income families 10 . Similarly, a survey of around 1,300 parents in the Netherlands found that during lockdown, children from working-class families had fewer computers at home and less room to study than upper/middle-class children 11 .

Data from non-Western countries highlight a more general digital divide, showing that developing countries have poorer access to digital equipment. For example, in India in 2018, only 10.7% of households possessed a digital device 25 , while in Pakistan in 2020, 31% of higher-education teachers did not have Internet access and 68.4% did not have a laptop 26 . In general, developing countries lack access to digital technologies 27 , 28 , and these difficulties of access are even greater in rural areas (for example, see ref. 29 ). Consequently, school closures have huge repercussions for the continuity of learning in these countries. For example, in India in 2018, only 11% of the rural and 40% of the urban population above 14 years old could use a computer and access the Internet 25 . Time spent on education during school closure decreased by 80% in Bangladesh 30 . A similar trend was observed in other countries 31 , with only 22% of children engaging in remote learning in Kenya 32 and 50% in Burkina Faso 33 . In Ghana, 26–32% of children spent no time at all on learning during the pandemic 34 . Beyond the overall digital divide, social class disparities are also evident in developing countries, with lower access to digital resources among households in which parental educational levels were low (versus households in which parental educational levels were high; for example, see ref. 35 for Nigeria and ref. 31 for Ecuador).

Unequal digital skills

In addition to unequal access to digital tools, there are also systematic variations in digital skills 36 , 37 (Fig. 1 ). Upper/middle-class families are more familiar with digital tools and resources and are therefore more likely to have the digital skills needed for distance learning 38 , 39 , 40 . These digital skills are particularly useful during school closures, both for students and for parents, for organizing, retrieving and correctly using the resources provided by the teachers (for example, sending or receiving documents by email, printing documents or using word processors).

Social class disparities in digital skills can be explained in part by the fact that children from upper/middle-class families have the opportunity to develop digital skills earlier than working-class families 41 . In member countries of the OECD (Organisation for Economic Co-operation and Development), only 23% of working-class children had started using a computer at the age of 6 years or earlier compared with 43% of upper/middle-class children 42 . Moreover, because working-class people tend to persist less than upper/middle-class people when confronted with digital difficulties 23 , the use of digital tools and resources for distance learning may interfere with the ability of parents to help children with their schoolwork.

Unequal use of digital tools

A third level of digital divide concerns variations in digital tool use 18 , 43 (Fig. 1 ). Upper/middle-class families are more likely to use digital resources for work and education 6 , 41 , 44 , whereas working-class families are more likely to use these resources for entertainment, such as electronic games or social media 6 , 45 . This divide is also observed among students, whereby working-class students tend to use digital technologies for leisure activities, whereas their upper/middle-class peers are more likely to use them for academic activities 46 and to consider that computers and the Internet provide an opportunity for education and training 23 . Furthermore, working-class families appear to regulate the digital practices of their children less 47 and are more likely to allow screens in the bedrooms of children and teenagers without setting limits on times or practices 48 .

In sum, inequalities in terms of digital resources, skills and use have strong implications for distance learning. This is because they make working-class students and parents particularly vulnerable when learning relies on extensive use of digital devices rather than on face-to-face interaction with teachers.

The cultural divide

Even if all three levels of digital divide were closed, upper/middle-class families would still be better prepared than working-class families to ensure educational continuity for their children. Upper/middle-class families are more familiar with the academic knowledge and skills that are expected and valued in educational settings, as well as with the independent, autonomous way of learning that is valued in the school culture and becomes even more important during school closure (Fig. 1 ).

Unequal familiarity with academic knowledge and skills

According to classical social reproduction theory 8 , 49 , school is not a neutral place in which all forms of language and knowledge are equally valued. Academic contexts expect and value culture-specific and taken-for-granted forms of knowledge, skills and ways of being, thinking and speaking that are more in tune with those developed through upper/middle-class socialization (that is, ‘cultural capital’ 8 , 50 , 51 , 52 , 53 ). For instance, academic contexts value interest in the arts, museums and literature 54 , 55 , a type of interest that is more likely to develop through socialization in upper/middle-class families than in working-class socialization 54 , 56 . Indeed, upper/middle-class parents are more likely than working-class parents to engage in activities that develop this cultural capital. For example, they possess more books and cultural objects at home, read more stories to their children and visit museums and libraries more often (for examples, see refs. 51 , 54 , 55 ). Upper/middle-class children are also more involved in extra-curricular activities (for example, playing a musical instrument) than working-class children 55 , 56 , 57 .

Beyond this implicit familiarization with the school curriculum, upper/middle-class parents more often organize educational activities that are explicitly designed to develop academic skills of their children 57 , 58 , 59 . For example, they are more likely to monitor and re-explain lessons or use games and textbooks to develop and reinforce academic skills (for example, labelling numbers, letters or colours 57 , 60 ). Upper/middle-class parents also provide higher levels of support and spend more time helping children with homework than working-class parents (for examples, see refs. 61 , 62 ). Thus, even if all parents are committed to the academic success of their children, working-class parents have fewer chances to provide the help that children need to complete homework 63 , and homework is more beneficial for children from upper-middle class families than for children from working-class families 64 , 65 .

School closures amplify the impact of cultural inequalities

The trends described above have been observed in ‘normal’ times when schools are open. School closures, by making learning rely more strongly on practices implemented at home (rather than at school), are likely to amplify the impact of these disparities. Consistent with this idea, research has shown that the social class achievement gap usually greatly widens during school breaks—a phenomenon described as ‘summer learning loss’ or ‘summer setback’ 66 , 67 , 68 . During holidays, the learning by children tends to decline, and this is particularly pronounced in children from working-class families. Consequently, the social class achievement gap grows more rapidly during the summer months than it does in the rest of the year. This phenomenon is partly explained by the fact that during the break from school, social class disparities in investment in activities that are beneficial for academic achievement (for example, reading, travelling to a foreign country or museum visits) are more pronounced.

Therefore, when they are out of school, children from upper/middle-class backgrounds may continue to develop academic skills unlike their working-class counterparts, who may stagnate or even regress. Research also indicates that learning loss during school breaks tends to be cumulative 66 . Thus, repeated episodes of school closure are likely to have profound consequences for the social class achievement gap. Consistent with the idea that school closures could lead to similar processes as those identified during summer breaks, a recent survey indicated that during the COVID-19 lockdown in the United Kingdom, children from upper/middle-class families spent more time on educational activities (5.8 h per day) than those from working-class families (4.5 h per day) 7 , 69 .

Unequal dispositions for autonomy and self-regulation

School closures have encouraged autonomous work among students. This ‘independent’ way of studying is compatible with the family socialization of upper/middle-class students, but does not match the interdependent norms more commonly associated with working-class contexts 9 . Upper/middle-class contexts tend to promote cultural norms of independence whereby individuals perceive themselves as autonomous actors, independent of other individuals and of the social context, able to pursue their own goals 70 . For example, upper/middle-class parents tend to invite children to express their interests, preferences and opinions during the various activities of everyday life 54 , 55 . Conversely, in working-class contexts characterized by low economic resources and where life is more uncertain, individuals tend to perceive themselves as interdependent, connected to others and members of social groups 53 , 70 , 71 . This interdependent self-construal fits less well with the independent culture of academic contexts. This cultural mismatch between interdependent self-construal common in working-class students and the independent norms of the educational institution has negative consequences for academic performance 9 .

Once again, the impact of these differences is likely to be amplified during school closures, when being able to work alone and autonomously is especially useful. The requirement to work alone is more likely to match the independent self-construal of upper/middle-class students than the interdependent self-construal of working-class students. In the case of working-class students, this mismatch is likely to increase their difficulties in working alone at home. Supporting our argument, recent research has shown that working-class students tend to underachieve in contexts where students work individually compared with contexts where students work with others 72 . Similarly, during school closures, high self-regulation skills (for example, setting goals, selecting appropriate learning strategies and maintaining motivation 73 ) are required to maintain study activities and are likely to be especially useful for using digital resources efficiently. Research has shown that students from working-class backgrounds typically develop their self-regulation skills to a lesser extent than those from upper/middle-class backgrounds 74 , 75 , 76 .

Interestingly, some authors have suggested that independent (versus interdependent) self-construal may also affect communication with teachers 77 . Indeed, in the context of distance learning, working-class families are less likely to respond to the communication of teachers because their ‘interdependent’ self leads them to respect hierarchies, and thus perceive teachers as an expert who ‘can be trusted to make the right decisions for learning’. Upper/middle class families, relying on ‘independent’ self-construal, are more inclined to seek individualized feedback, and therefore tend to participate to a greater extent in exchanges with teachers. Such cultural differences are important because they can also contribute to the difficulties encountered by working-class families.

The structural divide: unequal support from schools

The issues reviewed thus far all increase the vulnerability of children and students from underprivileged backgrounds when schools are closed. To offset these disadvantages, it might be expected that the school should increase its support by providing additional resources for working-class students. However, recent data suggest that differences in the material and human resources invested in providing educational support for children during periods of school closure were—paradoxically—in favour of upper/middle-class students (Fig. 1 ). In England, for example, upper/middle-class parents reported benefiting from online classes and video-conferencing with teachers more often than working-class parents 10 . Furthermore, active help from school (for example, online teaching, private tutoring or chats with teachers) occurred more frequently in the richest households (64% of the richest households declared having received help from school) than in the poorest households (47%). Another survey found that in the United Kingdom, upper/middle-class children were more likely to take online lessons every day (30%) than working-class students (16%) 12 . This substantial difference might be due, at least in part, to the fact that private schools are better equipped in terms of online platforms (60% of schools have at least one online platform) than state schools (37%, and 23% in the most deprived schools) and were more likely to organize daily online lessons. Similarly, in the United Kingdom, in schools with a high proportion of students eligible for free school meals, teachers were less inclined to broadcast an online lesson for their pupils 78 . Interestingly, 58% of teachers in the wealthiest areas reported having messaged their students or their students’ parents during lockdown compared with 47% in the most deprived schools. In addition, the probability of children receiving technical support from the school (for example, by providing pupils with laptops or other devices) is, surprisingly, higher in the most advantaged schools than in the most deprived 78 .

In addition to social class disparities, there has been less support from schools for African-American and Latinx students. During school closures in the United States, 40% of African-American students and 30% of Latinx students received no online teaching compared with 10% of white students 79 . Another source of inequality is that the probability of school closure was correlated with social class and race. In the United States, for example, school closures from September to December 2020 were more common in schools with a high proportion of racial/ethnic minority students, who experience homelessness and are eligible for free/discounted school meals 80 .

Similarly, access to educational resources and support was lower in poorer (compared with richer) countries 81 . In sub-Saharan Africa, during lockdown, 45% of children had no exposure at all to any type of remote learning. Of those who did, the medium was mostly radio, television or paper rather than digital. In African countries, at most 10% of children received some material through the Internet. In Latin America, 90% of children received some remote learning, but less than half of that was through the internet—the remainder being via radio and television 81 . In Ecuador, high-school students from the lowest wealth quartile had fewer remote-learning opportunities, such as Google class/Zoom, than students from the highest wealth quartile 31 .

Thus, the achievement gap and its accentuation during lockdown are due not only to the cultural and digital disadvantages of working-class families but also to unequal support from schools. This inequality in school support is not due to teachers being indifferent to or even supportive of social stratification. Rather, we believe that these effects are fundamentally structural. In many countries, schools located in upper/middle-class neighbourhoods have more money than those in the poorest neighbourhoods. Moreover, upper/middle-class parents invest more in the schools of their children than working-class parents (for example, see ref. 82 ), and schools have an interest in catering more for upper/middle-class families than for working-class families 83 . Additionally, the expectation of teachers may be lower for working-class children 84 . For example, they tend to estimate that working-class students invest less effort in learning than their upper/middle-class counterparts 85 . These differences in perception may have influenced the behaviour of teachers during school closure, such that teachers in privileged neighbourhoods provided more information to students because they expected more from them in term of effort and achievement. The fact that upper/middle-class parents are better able than working-class parents to comply with the expectations of teachers (for examples, see refs. 55 , 86 ) may have reinforced this phenomenon. These discrepancies echo data showing that working-class students tend to request less help in their schoolwork than upper/middle-class ones 87 , and they may even avoid asking for help because they believe that such requests could lead to reprimands 88 . During school closures, these students (and their families) may in consequence have been less likely to ask for help and resources. Jointly, these phenomena have resulted in upper/middle-class families receiving more support from schools during lockdown than their working-class counterparts.

Psychological effects of digital, cultural and structural divides

Despite being strongly influenced by social class, differences in academic achievement are often interpreted by parents, teachers and students as reflecting differences in ability 89 . As a result, upper/middle-class students are usually perceived—and perceive themselves—as smarter than working-class students, who are perceived—and perceive themselves—as less intelligent 90 , 91 , 92 or less able to succeed 93 . Working-class students also worry more about the fact that they might perform more poorly than upper/middle-class students 94 , 95 . These fears influence academic learning in important ways. In particular, they can consume cognitive resources when children and students work on academic tasks 96 , 97 . Self-efficacy also plays a key role in engaging in learning and perseverance in the face of difficulties 13 , 98 . In addition, working-class students are those for whom the fear of being outperformed by others is the most negatively related to academic performance 99 .

The fact that working-class children and students are less familiar with the tasks set by teachers, and less well equipped and supported, makes them more likely to experience feelings of incompetence (Fig. 1 ). Working-class parents are also more likely than their upper/middle-class counterparts to feel unable to help their children with schoolwork. Consistent with this, research has shown that both working-class students and parents have lower feelings of academic self-efficacy than their upper/middle-class counterparts 100 , 101 . These differences have been documented under ‘normal’ conditions but are likely to be exacerbated during distance learning. Recent surveys conducted during the school closures have confirmed that upper/middle-class families felt better able to support their children in distance learning than did working-class families 10 and that upper/middle-class parents helped their children more and felt more capable to do so 11 , 12 .

Pandemic disparity, future directions and recommendations

The research reviewed thus far suggests that children and their families are highly unequal with respect to digital access, skills and use. It also shows that upper/middle-class students are more likely to be supported in their homework (by their parents and teachers) than working-class students, and that upper/middle-class students and parents will probably feel better able than working-class ones to adapt to the context of distance learning. For all these reasons, we anticipate that as a result of school closures, the COVID-19 pandemic will substantially increase the social class achievement gap. Because school closures are a recent occurrence, it is too early to measure with precision their effects on the widening of the achievement gap. However, some recent data are consistent with this idea.

Evidence for a widening gap during the pandemic

Comparing academic achievement in 2020 with previous years provides an early indication of the effects of school closures during the pandemic. In France, for example, first and second graders take national evaluations at the beginning of the school year. Initial comparisons of the results for 2020 with those from previous years revealed that the gap between schools classified as ‘priority schools’ (those in low-income urban areas) and schools in higher-income neighbourhoods—a gap observed every year—was particularly pronounced in 2020 in both French and mathematics 102 .

Similarly, in the Netherlands, national assessments take place twice a year. In 2020, they took place both before and after school closures. A recent analysis compared progress during this period in 2020 in mathematics/arithmetic, spelling and reading comprehension for 7–11-year-old students within the same period in the three previous years 103 . Results indicated a general learning loss in 2020. More importantly, for the 8% of working-class children, the losses were 40% greater than they were for upper/middle-class children.

Similar results were observed in Belgium among students attending the final year of primary school. Compared with students from previous cohorts, students affected by school closures experienced a substantial decrease in their mathematics and language scores, with children from more disadvantaged backgrounds experiencing greater learning losses 104 . Likewise, oral reading assessments in more than 100 school districts in the United States showed that the development of this skill among children in second and third grade significantly slowed between Spring and Autumn 2020, but this slowdown was more pronounced in schools from lower-achieving districts 105 .

It is likely that school closures have also amplified racial disparities in learning and achievement. For example, in the United States, after the first lockdown, students of colour lost the equivalent of 3–5 months of learning, whereas white students were about 1–3 months behind. Moreover, in the Autumn, when some students started to return to classrooms, African-American and Latinx students were more likely to continue distance learning, despite being less likely to have access to the digital tools, Internet access and live contact with teachers 106 .

In some African countries (for example, Ethiopia, Kenya, Liberia, Tanzania and Uganda), the COVID-19 crisis has resulted in learning loss ranging from 6 months to more 1 year 107 , and this learning loss appears to be greater for working-class children (that is, those attending no-fee schools) than for upper/middle-class children 108 .

These findings show that school closures have exacerbated achievement gaps linked to social class and ethnicity. However, more research is needed to address the question of whether school closures differentially affect the learning of students from working- and upper/middle-class families.

Future directions

First, to assess the specific and unique impact of school closures on student learning, longitudinal research should compare student achievement at different times of the year, before, during and after school closures, as has been done to document the summer learning loss 66 , 109 . In the coming months, alternating periods of school closure and opening may occur, thereby presenting opportunities to do such research. This would also make it possible to examine whether the gap diminishes a few weeks after children return to in-school learning or whether, conversely, it increases with time because the foundations have not been sufficiently acquired to facilitate further learning 110 .

Second, the mechanisms underlying the increase in social class disparities during school closures should be examined. As discussed above, school closures result in situations for which students are unevenly prepared and supported. It would be appropriate to seek to quantify the contribution of each of the factors that might be responsible for accentuating the social class achievement gap. In particular, distinguishing between factors that are relatively ‘controllable’ (for example, resources made available to pupils) and those that are more difficult to control (for example, the self-efficacy of parents in supporting the schoolwork of their children) is essential to inform public policy and teaching practices.

Third, existing studies are based on general comparisons and very few provide insights into the actual practices that took place in families during school closure and how these practices affected the achievement gap. For example, research has documented that parents from working-class backgrounds are likely to find it more difficult to help their children to complete homework and to provide constructive feedback 63 , 111 , something that could in turn have a negative impact on the continuity of learning of their children. In addition, it seems reasonable to assume that during lockdown, parents from upper/middle-class backgrounds encouraged their children to engage in practices that, even if not explicitly requested by teachers, would be beneficial to learning (for example, creative activities or reading). Identifying the practices that best predict the maintenance or decline of educational achievement during school closures would help identify levers for intervention.

Finally, it would be interesting to investigate teaching practices during school closures. The lockdown in the spring of 2020 was sudden and unexpected. Within a few days, teachers had to find a way to compensate for the school closure, which led to highly variable practices. Some teachers posted schoolwork on platforms, others sent it by email, some set work on a weekly basis while others set it day by day. Some teachers also set up live sessions in large or small groups, providing remote meetings for questions and support. There have also been variations in the type of feedback given to students, notably through the monitoring and correcting of work. Future studies should examine in more detail what practices schools and teachers used to compensate for the school closures and their effects on widening, maintaining or even reducing the gap, as has been done for certain specific literacy programmes 112 as well as specific instruction topics (for example, ecology and evolution 113 ).

Practical recommendations

We are aware of the debate about whether social science research on COVID-19 is suitable for making policy decisions 114 , and we draw attention to the fact that some of our recommendations (Table 1 ) are based on evidence from experiments or interventions carried out pre-COVID while others are more speculative. In any case, we emphasize that these suggestions should be viewed with caution and be tested in future research. Some of our recommendations could be implemented in the event of new school closures, others only when schools re-open. We also acknowledge that while these recommendations are intended for parents and teachers, their implementation largely depends on the adoption of structural policies. Importantly, given all the issues discussed above, we emphasize the importance of prioritizing, wherever possible, in-person learning over remote learning 115 and where this is not possible, of implementing strong policies to support distance learning, especially for disadvantaged families.

Where face-to face teaching is not possible and teachers are responsible for implementing distance learning, it will be important to make them aware of the factors that can exacerbate inequalities during lockdown and to provide them with guidance about practices that would reduce these inequalities. Thus, there is an urgent need for interventions aimed at making teachers aware of the impact of the social class of children and families on the following factors: (1) access to, familiarity with and use of digital devices; (2) familiarity with academic knowledge and skills; and (3) preparedness to work autonomously. Increasing awareness of the material, cultural and psychological barriers that working-class children and families face during lockdown should increase the quality and quantity of the support provided by teachers and thereby positively affect the achievements of working-class students.

In addition to increasing the awareness of teachers of these barriers, teachers should be encouraged to adjust the way they communicate with working-class families due to differences in self-construal compared with upper/middle-class families 77 . For example, questions about family (rather than personal) well-being would be congruent with interdependent self-construals. This should contribute to better communication and help keep a better track of the progress of students during distance learning.

It is also necessary to help teachers to engage in practices that have a chance of reducing inequalities 53 , 116 . Particularly important is that teachers and schools ensure that homework can be done by all children, for example, by setting up organizations that would help children whose parents are not in a position to monitor or assist with the homework of their children. Options include homework help groups and tutoring by teachers after class. When schools are open, the growing tendency to set homework through digital media should be resisted as far as possible given the evidence we have reviewed above. Moreover, previous research has underscored the importance of homework feedback provided by teachers, which is positively related to the amount of homework completed and predictive of academic performance 117 . Where homework is web-based, it has also been shown that feedback on web-based homework enhances the learning of students 118 . It therefore seems reasonable to predict that the social class achievement gap will increase more slowly (or even remain constant or be reversed) in schools that establish individualized monitoring of students, by means of regular calls and feedback on homework, compared with schools where the support provided to pupils is more generic.

Given that learning during lockdown has increasingly taken place in family settings, we believe that interventions involving the family are also likely to be effective 119 , 120 , 121 . Simply providing families with suitable material equipment may be insufficient. Families should be given training in the efficient use of digital technology and pedagogical support. This would increase the self-efficacy of parents and students, with positive consequences for achievement. Ideally, such training would be delivered in person to avoid problems arising from the digital divide. Where this is not possible, individualized online tutoring should be provided. For example, studies conducted during the lockdown in Botswana and Italy have shown that individual online tutoring directly targeting either parents or students in middle school has a positive impact on the achievement of students, particularly for working-class students 122 , 123 .

Interventions targeting families should also address the psychological barriers faced by working-class families and children. Some interventions have already been designed and been shown to be effective in reducing the social class achievement gap, particularly in mathematics and language 124 , 125 , 126 . For example, research showed that an intervention designed to train low-income parents in how to support the mathematical development of their pre-kindergarten children (including classes and access to a library of kits to use at home) increased the quality of support provided by the parents, with a corresponding impact on the development of mathematical knowledge of their children. Such interventions should be particularly beneficial in the context of school closure.

Beyond its impact on academic performance and inequalities, the COVID-19 crisis has shaken the economies of countries around the world, casting millions of families around the world into poverty 127 , 128 , 129 . As noted earlier, there has been a marked increase in economic inequalities, bringing with it all the psychological and social problems that such inequalities create 130 , 131 , especially for people who live in scarcity 132 . The increase in educational inequalities is just one facet of the many difficulties that working-class families will encounter in the coming years, but it is one that could seriously limit the chances of their children escaping from poverty by reducing their opportunities for upward mobility. In this context, it should be a priority to concentrate resources on the most deprived students. A large proportion of the poorest households do not own a computer and do not have personal access to the Internet, which has important consequences for distance learning. During school closures, it is therefore imperative to provide such families with adequate equipment and Internet service, as was done in some countries in spring 2020. Even if the provision of such equipment is not in itself sufficient, it is a necessary condition for ensuring pedagogical continuity during lockdown.

Finally, after prolonged periods of school closure, many students may not have acquired the skills needed to pursue their education. A possible consequence would be an increase in the number of students for whom teachers recommend class repetitions. Class repetitions are contentious. On the one hand, class repetition more frequently affects working-class children and is not efficient in terms of learning improvement 133 . On the other hand, accepting lower standards of academic achievement or even suspending the practice of repeating a class could lead to pupils pursuing their education without mastering the key abilities needed at higher grades. This could create difficulties in subsequent years and, in this sense, be counterproductive. We therefore believe that the most appropriate way to limit the damage of the pandemic would be to help children catch up rather than allowing them to continue without mastering the necessary skills. As is being done in some countries, systematic remedial courses (for example, summer learning programmes) should be organized and financially supported following periods of school closure, with priority given to pupils from working-class families. Such interventions have genuine potential in that research has shown that participation in remedial summer programmes is effective in reducing learning loss during the summer break 134 , 135 , 136 . For example, in one study 137 , 438 students from high-poverty schools were offered a multiyear summer school programme that included various pedagogical and enrichment activities (for example, science investigation and music) and were compared with a ‘no-treatment’ control group. Students who participated in the summer programme progressed more than students in the control group. A meta-analysis 138 of 41 summer learning programmes (that is, classroom- and home-based summer interventions) involving children from kindergarten to grade 8 showed that these programmes had significantly larger benefits for children from working-class families. Although such measures are costly, the cost is small compared to the price of failing to fulfil the academic potential of many students simply because they were not born into upper/middle-class families.

The unprecedented nature of the current pandemic means that we lack strong data on what the school closure period is likely to produce in terms of learning deficits and the reproduction of social inequalities. However, the research discussed in this article suggests that there are good reasons to predict that this period of school closures will accelerate the reproduction of social inequalities in educational achievement.

By making school learning less dependent on teachers and more dependent on families and digital tools and resources, school closures are likely to greatly amplify social class inequalities. At a time when many countries are experiencing second, third or fourth waves of the pandemic, resulting in fresh periods of local or general lockdowns, systematic efforts to test these predictions are urgently needed along with steps to reduce the impact of school closures on the social class achievement gap.

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We thank G. Reis for editing the figure. The writing of this manuscript was supported by grant ANR-19-CE28-0007–PRESCHOOL from the French National Research Agency (S.G.).

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Goudeau, S., Sanrey, C., Stanczak, A. et al. Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap. Nat Hum Behav 5 , 1273–1281 (2021). https://doi.org/10.1038/s41562-021-01212-7

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Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States

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  • Matthew H. E. M. Browning, 
  • Lincoln R. Larson, 
  • Iryna Sharaievska, 
  • Alessandro Rigolon, 
  • Olivia McAnirlin, 
  • Lauren Mullenbach, 
  • Scott Cloutier, 
  • Tue M. Vu, 
  • Jennifer Thomsen, 

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  • Published: January 7, 2021
  • https://doi.org/10.1371/journal.pone.0245327
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26 Aug 2022: Browning MHEM, Larson LR, Sharaievska I, Rigolon A, McAnirlin O, et al. (2022) Correction: Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States. PLOS ONE 17(8): e0273938. https://doi.org/10.1371/journal.pone.0273938 View correction

Table 1

University students are increasingly recognized as a vulnerable population, suffering from higher levels of anxiety, depression, substance abuse, and disordered eating compared to the general population. Therefore, when the nature of their educational experience radically changes—such as sheltering in place during the COVID-19 pandemic—the burden on the mental health of this vulnerable population is amplified. The objectives of this study are to 1) identify the array of psychological impacts COVID-19 has on students, 2) develop profiles to characterize students' anticipated levels of psychological impact during the pandemic, and 3) evaluate potential sociodemographic, lifestyle-related, and awareness of people infected with COVID-19 risk factors that could make students more likely to experience these impacts.

Cross-sectional data were collected through web-based questionnaires from seven U.S. universities. Representative and convenience sampling was used to invite students to complete the questionnaires in mid-March to early-May 2020, when most coronavirus-related sheltering in place orders were in effect. We received 2,534 completed responses, of which 61% were from women, 79% from non-Hispanic Whites, and 20% from graduate students.

Exploratory factor analysis on close-ended responses resulted in two latent constructs, which we used to identify profiles of students with latent profile analysis, including high (45% of sample), moderate (40%), and low (14%) levels of psychological impact. Bivariate associations showed students who were women, were non-Hispanic Asian, in fair/poor health, of below-average relative family income, or who knew someone infected with COVID-19 experienced higher levels of psychological impact. Students who were non-Hispanic White, above-average social class, spent at least two hours outside, or less than eight hours on electronic screens were likely to experience lower levels of psychological impact. Multivariate modeling (mixed-effects logistic regression) showed that being a woman, having fair/poor general health status, being 18 to 24 years old, spending 8 or more hours on screens daily, and knowing someone infected predicted higher levels of psychological impact when risk factors were considered simultaneously.

Inadequate efforts to recognize and address college students’ mental health challenges, especially during a pandemic, could have long-term consequences on their health and education.

Citation: Browning MHEM, Larson LR, Sharaievska I, Rigolon A, McAnirlin O, Mullenbach L, et al. (2021) Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States. PLoS ONE 16(1): e0245327. https://doi.org/10.1371/journal.pone.0245327

Editor: Chung-Ying Lin, Hong Kong Polytechnic University, HONG KONG

Received: August 4, 2020; Accepted: December 28, 2020; Published: January 7, 2021

Copyright: © 2021 Browning 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: All relevant data are within the manuscript and its Supporting Information files.

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

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

1 Introduction

A large number of studies support that the conclusion that the novel coronavirus (SARS-CoV-2) and its corresponding disease (COVID-19) have dramatically impacted people's mental health and behavior [ 1 – 5 ], with very few studies suggesting otherwise [ 6 ]. Mental health hotlines in the United States experienced 1,000% increases during the month of April, when most people were under lockdown because of the pandemic [ 7 ]. Some medical facilities have seen more deaths from suicide, presumably because of exceedingly poor mental health, than from COVID-19 infections [ 8 ]. Substance disorders in many people who were previously abstinent are expected to relapse during COVID-19, which will cause long-term economic and health impacts [ 9 ].

Although impacts are felt across populations—and especially in socially-disadvantaged communities and individuals employed as essential workers—college students are among the most strongly affected by COVID-19 because of uncertainty regarding academic success, future careers, and social life during college, amongst other concerns [ 10 ]. Even before the pandemic, students across the globe experienced increasing levels of anxiety, depressive moods, lack of self-esteem, psychosomatic problems, substance abuse, and suicidality [ 11 ]. Therefore, students may need additional resources and services to deal with the physical and mental health repercussions of the disease.

University administrators could best serve students if they better understood the impacts of COVID-19 and the risk factors of its psychological impacts. These impacts are of critical importance to warrant immediate mental health interventions focused on prevention and treatment [ 12 ]. Psychiatric and counseling services have historically been underutilized by college students [ 13 , 14 ]. Understanding what subpopulations may suffer from unique combinations of psychological impacts may facilitate targeted interventions and successful treatment and coping strategies for individuals at greatest risk.

A recent review highlights some of the documented psychological impacts of COVID-19 on college students [ 15 ]. Many feel increased stress levels and anxiety and depressive symptoms as a result of changed delivery and uncertainty of university education, technological concerns of online courses, being far from home, social isolation, decreased family income, and future employment. These impacts have been observed in universities across the world [ 10 ].

Studies of the psychological impacts of COVID-19 on college studies in the United States, however, have been limited in their generalizability [ 10 ] due to examination of single institutions only [ 10 , 16 , 17 ]. We are aware of no studies that have been conducted with college students at multiple institutions across the United States during the pandemic. These schools collectively represent a somewhat unique context within higher education. The United States educates large numbers of students from around the world [ 18 , 19 ]. Diverse student bodies may show different risk factors from more culturally-homogenous student bodies because of the diversity of value orientations [ 20 ] and sources of media consumption [ 16 , 21 – 23 ]. Further, colleges in the United States cost more than higher education institutions nearly anywhere else in the world [ 24 ]; therefore, financial concerns may be particularly apparent in the United States. The United States also experienced the lowest global recovery rate from infection–in other words, the highest mortality rate post-infection–in the weeks leading up to the timing of the current study (April and May, 2020) [ 25 ]. This country continues to witness the highest incidence and mortality rates among Global North countries [ 26 ]. Such high rates aggravate the psychological impacts of the disease on infected and non-infected individuals [ 1 ].

In the current study, we investigate the psychological impacts of COVID-19 and associated risk factors on college students at seven universities across the United States. Our objectives are three-fold: 1) identify the array of psychological impacts COVID-19 has on students, 2) develop profiles to characterize students' anticipated levels of psychological impact during the pandemic, and 3) evaluate potential sociodemographic, lifestyle-related, and awareness of people infected with COVID-19 risk factors that could make students more likely to experience these impacts.

2.1 Study population

In spring 2020, 14,174 participants were recruited cross-sectionally from representative and targeted samples at seven large, state universities, which in sum enroll more than 238,000 students. Universities included Arizona State University in Tempe, AZ (approximately 52,000 undergraduate/graduate students enrolled in 2019); Clemson University in Clemson, SC (approx. 25,000); North Carolina State University in Raleigh, NC (approx. 34,000); Oregon State University in Corvallis, OR (approx. 29,000); Pennsylvania State University in State College, PA (approx. 54,000); University of Montana in Missoula, MT (approx. 11,000); and The University of Utah in Salt Lake City, UT (approx. 33,000). One institution (North Carolina State University) was able to utilize a university-wide representative sample. Other institutions used targeted samples in the home college(s) or department(s) of the corresponding author. Selection of sampling scheme (i.e., representative or targeted) was determined by human subject review board allowances and listserv availability. (Recruitment occurred over email listservs and course website announcements.)

This research was deemed exempt from the Clemson University Institutional Review Board. Also, all subjects provided written consent when they completed the online survey.

Recruitment started as soon as human subject approval was awarded and occurred over a two-to-three-week window at each institution. Because approval took longer at some institutions, nationwide recruitment was staggered. No compensation for participation was provided. Sampling frames and recruitment windows are detailed in Table 1 .

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Of the 14,174 students invited to participate in the survey, we received 2,534 responses with data on most of the relevant variables; thus, this sample size was available for most of the descriptive statistics and bivariate associations. Missing/not reported data on race/ethnicity and gender occurred in approximately 11% of respondents. Therefore, complete data for multivariate analyses with all risk factors entered simultaneously—including race/ethnicity and gender—were available for 2,140 students. Table 2 provides the sample characteristics.

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2.2 Measures

2.2.1 psychological impacts..

2 . 2 . 1 . 1 Qualitative assessment . We expected that it would be difficult to parsimoniously and comprehensively capture the broad array of impacts from COVID-19 on students with quantitative measures. Therefore, we utilized an open-ended questionnaire item that asked respondents, "We are interested in the ways that the coronavirus (COVID-19) pandemic has changed how you feel and behave. What are the first three ways that come to mind?" Three responses were required, and a fourth response was optional. This question was placed at the beginning of the questionnaire to avoid priming and order effects [ 27 , 28 ].

2 . 2 . 1 . 2 Quantitative assessment . Regarding our selection of quantitative impacts to measure, we chose nine survey items based on information gathered from a review of previous research and new interview data. These nine survey items measured the following concepts: negative emotion states, preoccupation with COVID-19, feeling stressed, worry, and time demands.

Regarding the review of previous research, we examined studies of other large-scale disasters (i.e., the World Trade Center terrorist attacks on September 11, 2001; previous epidemics requiring quarantine), which are almost always associated with psychological impacts on the general population [ 29 ]. These studies provided some guidance on what impacts to measure for the impacts of COVID-19 on college students.

Regarding new interview data, the corresponding author of the current study conducted unstructured interviews with adults on their experiences in the early stages of the COVID-19 pandemic. These interviews consisted of recruiting ten participants aged 18 years or older in February 2020. Recruitment occurred in both low-risk and high-risk regions of the United States, including urban areas in Washington and rural areas in Tennessee, Iowa, and South Carolina. The interviews captured the feelings that interviewees experienced during the pandemic.

Negative emotion states comprised four of the survey items. Each item explained one of the basic negative emotions (i.e., being afraid, irritable, guilty, and sad) identified during the development of the positive and negative affect schedule (PANAS) [ 30 ]. Items were measured using the visual analogue scale (VAS) to provide data across a wide range of responses (1–100) with minimal participant burden [ 31 ]. Prompts asked respondents to indicate the extent to which they felt these things when they thought about the pandemic.

Preoccupation and feeling stressed comprised two more survey items. These were also measured with the VAS. Prompts once again asked respondents to indicate the extent to which they felt these things when they thought about the pandemic.

One more survey item measured worry—specifically anxious arousal. It was measured with a single item ("I worry a lot") from the Penn State Worry Questionnaire (PSWQ) that is strongly associated with the entire 16-item PSWQ, r = 0.80 [ 32 ]. Therefore, this single item succinctly captures the concept of worry/anxious arousal. A 5-point Likert-type agree-disagree response scale was used.

Two more survey items measured time demands. These were developed from survey prompts in the eating disorder literature [ 33 ]. Specifically, we asked to what extent respondents believed they spent a lot of time/thought on the pandemic, and to what extent they believed they spent too much time/thought on the pandemic. Once again, a 5-point Likert-type agree-disagree response scale was used.

The prompts for all nine of these survey items were delivered as reactions to the coronavirus rather than measures of general psychological states. Example include: "how stressed do you feel when you think about coronavirus," and "to what extent do you agree/disagree with the following: I worry about coronavirus all of the time."

2.2.2 Risk factors.

Sociodemographic factors were self-reported and allowed identification of potential differences in impact levels by gender, age, race/ethnicity, socioeconomic status (SES), and academic status (undergraduate vs. graduate-seeking). SES was measured with perceived social class, which has been shown to accurately represent SES in student populations, using a battery of seven questions on class, parental education, and relative family income [ 34 , 35 ]. To measure academic status, we asked respondents whether they were in pursuit of an undergraduate or graduate degree.

To account for possible lifestyle-related risk factors, we first considered general health factors such as general health status and body mass index (BMI). Health status was measured with a single item on respondents' "health in general" and a 5-point response scale (poor to excellent) [ 36 ]. BMI was calculated from self-reported height and weight. BMI has been implicated as a risk factor or confounder of the psychological impacts of COVID-19 [ 37 , 38 ].

Another set of plausible lifestyle-related risk factors was time use. We utilized a recent recall question structure from the American Time Use Survey that strongly predicts objective time use and activity measures [ 39 ]. Three items were used to ask respondents to indicate how many hours they spent outdoors (at a park, on a greenway/trail, in a neighborhood/yard, etc.), in front of a screen (on a smartphone/computer, watching television, online gaming, etc.), and engaged in moderate or vigorous physical activity that caused an increase in breathing or heart rate (fast walking, running, etc.) in the past 24 hours [ 40 , 41 ].

Regarding awareness of COVID-19 victims as a potential risk factor, we included two measures of knowing people who were diagnosed with the virus: someone in their family and someone in their community [ 42 ].

2.3 Analyses

To accomplish Objective 1, qualitative data from the open-ended responses were analyzed using content analysis with an inductive approach [ 43 , 44 ]. Two independent researchers examined the data systematically to identify patterns and codes [ 43 ]. Each response was coded separately and reviewed for agreement [ 45 , 46 ]. Interrater/intercoder agreement (kappa) score was 94.94% [ 47 ].

Objective 2 was accomplished in three steps. These included data imputations, data reduction, and profile identification.

We imputed missing values by bag imputation, which fits a machine learning regression tree model for each predictor as a function of all others [ 48 ]. In our dataset, 5.2% of the quantitative data were missing and imputed.

Next, we reduced the survey items related to levels of psychological impact into latent constructs using exploratory factor analysis (EFA) with oblimin rotation [ 49 ]. Scree plots and Very Simple Structure (VSS) criterion were used to identify the number of factors [ 50 ]. The VSS criterion evaluates the magnitude of the changes in goodness of fit with each increase in the number of extracted factors.

Last, using the composite scores from the EFA, we used the identified latent constructs from the psychological impact survey items as input variables in a latent profile analysis (LPA) [ 51 ]. Criteria for determining the number of profiles in the LPA included statistical adequacy of the solution and interpretability of each profile [ 52 ]. Indices used to determine statistical adequacy included the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sample-size adjusted Bayesian Information Criterion [ 53 ]. For each of these indices, lower values represented better model fit. Also, the entropy criterion was calculated as a measure of classification precision [ 54 ]. We favored a parsimonious solution with fewer profiles over a more complex solution if this improved the interpretability of the LPA [ 53 ]. Z-scores of the input variables were used to interpret the profiles. The criteria to assign low and high values is not established and so we adopted previous studies' thresholds [ 53 ]. These included standardized scores between +0.5 and -0.5 being labelled as moderate, scores above 0.5 being labelled as high, and scores below -0.5 being labelled as low levels of psychological impact from COVID-19.

Objective 3 was achieved by modeling unadjusted (bivariate) and adjusted (multivariate) relationships between risk factors and profiles from the LPA. Unadjusted results are presented because multivariate models used a dichotomous outcome variable to distinguish students in the highest profile of psychological impact from those in the moderate or low profiles of impact (see Results for profile development and sample sizes within each profile). Determining risk factors for being in the high impact profile was deemed more important and actionable by university administrators than determining risk factors for each of the lower impact profiles, as would have been accomplished with a multinomial model. Thus, this modeling approach served a practical function; results could inform university administrators with tight budgets on how to prioritize funding for mental health interventions amongst students at greatest risk of high levels of psychological impact. Unadjusted results remained relevant, however, since they served the function of comparing risk factors between each level of impact profile in a simpler format than the output of a multinomial regression model.

For the unadjusted results, risk factors were evaluated with chi-squared contingency tables. Residuals from observed versus expected count comparisons determined the direction of the effect of the risk factors (i.e., more or less likely that a group was classified to a higher impact profile than another profile). Statistical significance of risk factors was calculated with Bonferroni adjustments to reduce Type I Error [ 55 ]. Continuous measures were reduced to dichotomous or categorical factors based on clinically meaningful levels, past research, and data distributions. BMI was classified into four categories (less than 18 = underweight; 18 to 24.99 = normal; 25 to 29.99 = overweight; 30.0 and over = obese) [ 56 ]. General health was separated into two groups: poor/fair health and good/very good/excellent health [ 53 ]. Screen time was separated into less than eight hours on a device and eight or more hours on a device [ 57 ]. Time outdoors was split into three groups: Less than 1.00 hour, 1.00 to 1.99 hours, and 2.00 hours or more [ 58 , 59 ]. Time spent exercising was also split into three groups: 30.00 minutes or less, 30.01 to 59.99 minutes, and 1.00 hour or more [ 60 ]. In addition, social class and relative income were split into three levels: below average, average, or above average. Levels of education were split into two levels: less than a 4-year college degree and a 4-year college degree or more [ 61 ].

For the adjusted results, we conducted generalized mixed-effects logistic regression to examine risk factors simultaneously and control for random (grouping) effects by institutional affiliation. To avoid collinearity in SES measures, whichever item correlated most strongly with psychological impacts was entered in the model. We used Variance Inflation Factor (VIF) values to test for multicollinearity. The proportion of variance explained was measured with conditional and marginal R 2 coefficients of determination [ 62 – 64 ]. Marginal R 2 represents the contribution of the predictors, which are modelled as fixed effects, whereas conditional R 2 accounts for the additional contribution of institutional affiliation (random effect) in addition to the fixed effects.

As a sensitivity analysis, we ran a logistic regression model with a subsample of respondents from the university that obtained a representative sample (North Carolina State University). This allowed us to evaluate the robustness of our nationwide sample, which otherwise utilized a convenience sampling approach.

Analyses were conducted in Excel for Mac Version 16.38 and R Version 3.6.2.

3.1 Array of impacts

Qualitative data from the open-ended responses demonstrated a broad array of impacts from COVID-19 on college students’ feelings ( Table 3 ) and behaviors ( Table 4 ). The most common changes in how students felt compared to before the pandemic were increased lack of motivation, anxiety, stress, and isolation. For example, one of the students reflected, “I'm normally extremely motivated, and I've never struggled with depression, but have recently felt very sluggish and melancholy.” Another student described their feelings related to isolation as “I feel trapped. I don't have anywhere I need to go since I can't socialize, and I have schoolwork. But yet I still feel trapped due to actual restrictions and suggestions.” The most frequent changes in student behavior compared to before the pandemic included more social distancing, more education changes, and less going out. Other concerning changes ranged from entrapment, boredom, fatigue, hopelessness, guilt, and inconvenience to hygiene, sleep, housing, employment, personal finances, and caretaking. For example, some students expressed their frustration with the financial situation, including one statement indicating: “I am BROKE. I lost my job because of this pandemic and now I can’t pay for groceries.” Other students were concerned about online learning. For example, one student commented: “I am constantly on edge about coursework: Did the computer register I submitted my exam? Did I see everything my teacher posted in Moodle? What happens if my internet goes out and I miss an assignment?”

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Smaller numbers of students reported positive changes from the COVID-19 pandemic as well. These included optimism, productivity, adaptation, and empathy, as highlighted in the following quotes: “I've affirmed that people are capable of adapting in any circumstances” and “[I felt a] higher degree of empathy toward my community”.

3.2 Psychological impact profiles

Mean values of the psychological impact survey items are shown in Table 5 . Eight of these were included in the EFA. (Feeling guilty demonstrated low communality ( h 2 = .21) and was removed from further analyses.) All eight items displayed relatively normal distributions ( S1 Fig ). Criteria of the resulting model were acceptable: Tucker Lewis Index = 0.95; Kaiser-Meyer-Olkin (KMO) factor adequacy measure of sampling adequacy (MSA) = .89 [ 65 ]; significant Bartlett's test of sphericity, χ 2 (28) = 10503, p < .001. The VSS Criterion [ 50 ] achieved a maximum of .93 with a two-factor solution, compared to .89 for a one-factor solution or .94 for a three-factor solution ( S1 Table , S2 and S3 Figs). We labelled the first factor as "Emotional Distress" since it was composed largely of negative affect items (afraid, irritable, sad, preoccupied and stressed). The second factor was composed of three items dealing with how time was spent presumably in worry during the pandemic (worry, too much time and a lot of time), and so we labelled it "Worry Time." This is a term from clinical psychology that describes time spent reflecting on all the possible impacts of a health concern, including those worries that an individual cannot do anything about [ 66 ]. The internal reliability of the factors was high, Cronbach's ⍺ = .87 for Worry Time and .83 for Emotional Distress.

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A three-profile solution fit the data best for the LPA. Information criteria decreased with additional profiles up to a five-profile solution, indicating a better model fit ( S2 Table , S4 Fig ). The elbow plot suggested minor improvements in model fit after a three-profile solution. Adding a fourth or fifth profile provided less interpretable results. Based on the combined information from the statistical criteria and interpretability, we retained a three-profile solution as our final model.

The three levels of psychological impact from COVID-19 resulting from the LPA are depicted in Fig 1 . Positive z-scores indicate higher levels of impact and negative z-scores indicate lower levels of impact, compared to the average. Profile 1 ("high") represented students with higher than average levels of the two factors measuring psychological impacts (Emotional Distress, Worry Time) stemming from COVID-19. Profile 2 ("moderate") represented students with moderate levels of the two factors, and profile 3 ("low") represented students with low levels of the two factors. Regarding profile membership, 45.2% of students (n = 1,146) were within the high impact profile, whereas 40.4% (n = 1,025) were in the moderate profile and 14.3% (n = 363) were in the low profile.

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Means and standard errors shown.

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3.3 Risk factors

A summary of the risk factors with significant differences between impact profiles based on bivariate Chi-square tests is depicted in Fig 2 . With respect to sociodemographic factors, women were more likely to be at risk than men ( χ 2 (2) = 88, p < .001). Specifically, women were more likely to be in the high profile (residuals (RES) = 8.02, p < .001) and less likely to be in the moderate (RES = -2.75, p = .036) or low (RES = -7.54, p < .001) profile. Men demonstrated the opposite pattern. We did not observe differences by academic status ( χ 2 (2) = .3, p = .9), although we did observe differences by age ( χ 2 (4) = 15, p = .005). Students who were 18 to 24 years old were more likely to be in the moderate profile (RES = 3.81, p = .0013), and students who were 25 to 32 years old were less likely to be in the moderate profile (RES = -3.03, p = .022) than other profiles. No other significant differences between age groups by profile were found, p > .05.

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Sociodemographic (a), lifestyle (b), and COVID-19 victim awareness (c) risk factors associated with high, moderate, and low psychological impact profiles for students across the United States. Residuals from Pearson's chi-squared tests depict likelihood of profile membership based on risk factor. Only significant factors ( p < .05) are reported. Reference groups include men; over 32 age; other race/ethnicity; average/above average SES (social class and relative family income); good/very good/excellent general health; less than 2 hours of time outdoors; less than 8 hours of screen time; and not knowing someone infected (COVID-19).

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We also observed racial/ethnic and SES differences in psychological impact levels. Specifically, we found differences by race/ethnicity ( χ 2 (6) = 18, p = .007) with non-Hispanic Whites being more likely to be in the low profile (RES = 2.98, p = .035) and Non-Hispanic Asians being less likely to be in the low impact profile (RES = -3.42, p = .0076). No differences in impact profiles were observed for non-Hispanic Black students or Hispanic students, although sample sizes were small ( n = 95 and 98, respectively). Parental educational achievement measures further showed no differences in profiles, p = .5 for maternal and .9 for paternal. No differences were observed for parental social class either, p = .1 for maternal and .2 for paternal. In contrast, student social class ( χ 2 (4) = 14, p = .008), and relative family income ( χ 2 (4) = 14, p = .008) differed by impact profile. Students who reported above-average social class were more likely to be in the low profile (RES = 3.07, p = .019), and students who reported below-average relative family income were more likely to be in the high profile (RES = 3.38, p = .0065). No other significant differences between ethnoracial groups or SES measures by profile were found, p > .05.

Lifestyle-related factors predicted differences in impact profiles. For instance, general health predicted assignment to different impact profiles ( χ 2 (2) = 41, p < .001). Students with fair/poor health were more likely to be in the high profile (RES = 5.90, p < .001) and less likely to be in the moderate (RES = -2.67, p = .045) or low profile (RES = -4.58, p < .001). Students with good/very good/excellent health displayed the opposite pattern. No difference in impact profiles was observed for BMI ( χ 2 (6) = 9, p = .2). We observed differences in impact profiles by time outdoors ( χ 2 (4) = 13, p = .01) and screen time ( χ 2 (2) = 14, p = .001) but not by exercise time ( χ 2 (4) = 6, p = .2). Students who reported spending two or more hours outdoors were less likely to be in the high profile (RES = -3.17, p = .014), and students who reported spending more than eight hours on a device were more likely to be in the high profile (RES = 3.06, p = .013) and less likely to be in the moderate profile (RES = -3.67, p = .0014). Students spending less than eight hours on a device displayed the exact opposite trend. No other pair-wise comparisons in lifestyle-related factors were significant, p > .05.

Lastly, knowing someone who was infected with COVID-19 increased the likelihood of being at risk of psychological impacts ( χ 2 (2) = 14, p < .001). Students who knew someone in their family or community who was infected were more likely to be in the high profile (RES = 3.06, p = .013) and less likely to be in the moderate profile (RES = -3.67, p = .0014). Students who did not know an infected person displayed the opposite pattern.

Five variables remained significant predictors of impact profiles in models adjusting for all risk factors simultaneously while controlling for institutional affiliation ( Table 6 ). The SES measure entered in these models was social class of student, because it correlated more highly with psychological impact levels than other measures ( S5 Fig ). Students who were women, fair/poor general health, 18 to 24 years old, reporting 8 or more hours of screen time, and who knew someone infected with COVID-19 were more likely to be in the high profile. Non-Hispanic Asian students were marginally more likely to be in the high impact profile, p = .091. Effect sizes varied; women were approximately twice as likely to be assigned to the high impact profile as the moderate/low profile. Other predictors increased (or decreased) the likelihood of being in the high impact profile by approximately 20% to 40%. No institutions emerged as significant random effects ( S6 Fig ). VIF values < 2.0 indicated no multicollinearity. Approximately 7% of the variance was explained by the predictors and institutional affiliations.

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Sensitivity analyses with a subsample of respondents from the representative sample at North Carolina State University identified a similar set of predictors of psychological impact levels ( S3 Table ). Gender, age, general health, and knowing someone infected remained significant predictors. In contrast, screen time was no longer significant. Being Non-Hispanic Asian as marginally significant, p = .070, and social class was significant, p = .0038. Students of above average social class were 23.0% less likely to be assigned to the high impact profile.

4 Discussion

4.1 key findings and interpretation of results.

To evaluate the psychological impacts of COVID-19 on students in the United States, we collected over 2,500 survey responses from students at seven universities in late-February to mid-May 2020. Qualitative data from open-ended responses showed students experienced largely negative impacts of COVID-19 on psychological health and lifestyle behaviors. Among the most commonly reported changes were lack of motivation, anxiety, stress, and isolation, as well as social distancing, education changes, and going out less. Similar findings were reported by another study exploring the impact of COVID-19 on students at a single college in the United States, revealing increases in sedentary lifestyle, anxiety, and depressive symptoms [ 16 ]. A global study examining experiences of students in 62 countries, including one university in the United States, found that students’ expressed concerns about their academic and professional careers, as well as feelings of boredom, anxiety and frustration [ 10 ]. Increased anger, sadness, anxiety and fear were also reported by students in China [ 67 ]. Students in Switzerland reported a decrease in social interaction and higher levels of stress, anxiety, and loneliness [ 68 ]. More generally, adults have reported decreases in physical activity and food consumption increases during the COVID-19 pandemic quarantine compared to beforehand, as well as increases in binge drinking on average [ 69 ], which was identified in a small portion of our student respondents as well. Slight differences between our studies' results and results from studies conducted elsewhere may be due to the differences in student experience by geographical location. The United States is providing relatively little financial relief to college students during the pandemic compared to other Global North countries [ 70 ].

Quantitative survey measures captured the majority of the content that students entered in the open-ended responses (i.e., worry, stress, and fear) and informed the development of impact profiles. Students were assigned to one of three profiles—low (14% of the sample), moderate (40%) and high (45%)—based on the psychological impacts they reported experiencing in response to COVID-19.

In unadjusted models, students who were women, non-Hispanic Asian, in fair/poor health, of below-average relative family income, or someone who knew a family/community member infected with COVID-19 were at risk of higher levels of psychological impact. Students who were non-Hispanic White, above-average social class, spent at least two hours outside in the past day, or spent less than eight hours on screens in the past day were at less risk.

In multivariate models controlling, being a woman, being younger (18 to 24 years old), having poor/fair general health, reporting more screen time, and knowing someone infected were statistically significant risk factors. SES and identifying as non-Hispanic Asian were additional significant risk factors in the subsample of respondents obtained from representative sampling, whereas screen time was not significant in this sensitivity analysis.

These risk factor findings generally match those found in other studies that employed a case study approach within single United States universities/colleges. One longitudinal study of students at a public university in Nevada (n = 205) found that anxiety and depressive symptoms were greater in April 2020 than in prior months [ 17 ]. Women reported greater disruption to daily activities, mental and physical health, and personal finances than men. Contrary to our unadjusted findings, Asian or Asian-American students in the Nevada study reported lower levels of anxiety and depression than other races. A second longitudinal study with undergraduate students (n = 217) at a small liberal arts college in New Hampshire also found increases in anxiety, depression, and sedentary time during April 2020 relative to prior months [ 16 ]. COVID-19 risk factors for college students at other countries have been strikingly similar, as explained below.

Over ten studies, including several with college student populations, identify women as being at greater risk of psychological distress during the COVID-19 pandemic [ 1 , 10 , 21 , 71 – 77 ]. Women are generally prone to depression and anxiety disorders [ 14 ], and although initial evidence indicated men were more susceptible to infection [ 77 ], our study supports the assertion that women appear to be more strongly impacted by the long-term psychological impacts of the pandemic. This observation may be attributable to higher levels of pre-existing psychopathology in women as well as gender differences in fear processing, which could translate to exacerbations of symptoms [ 78 ]. Also, male students tend to have higher confidence in the computer skills necessary for the transition to online course delivery [ 10 ]. Meanwhile, women are more concerned about impacts on their professional career and ability to study than men, on average [ 10 ]. One study attributed these gender differences to greater emotional expression, less tolerance for uncertainty, and less-effective coping strategies amongst students who are women [ 75 ]. Women have also reported being more susceptible to "emotional hunger" and subsequent increased food intake than men during COVID-19 quarantine; these behaviors can lead to weight gain and poor mental health [ 73 ].

Our findings that fair/poor general health is a risk factor has been documented in numerous other populations during COVID-19 [ 79 , 80 ]. In addition to comorbidity between mental and physical health status, people with pre-existing health problems and those with poor mental health show lower preparedness for disasters and suffer disproportionately more from disaster-related outcomes [ 81 ].

Several reasons explain our findings that younger students may be at greater risk than older students. Younger students (i.e., 18 to 24 years old, regardless of academic status) tend to be more worried about their future education and ability to pay for college education than older students [ 10 ]. Younger people also engage in social media more than older people during the pandemic [ 12 , 82 ]. Given the dominance of the COVID-19 pandemic in the news, younger "always-on" students may be exposed to greater amounts of risk-elevating messages, which can lead to anxiety and poor mental health [ 16 , 75 ].

Regarding our findings that non-Hispanic Asian students may be at greater risk than other races/ethnicities, several studies show higher psychological distress from COVID-19 in this population [ 10 ]. Asians and Asian Americans have reported being discriminated against by other students on social media during the pandemic [ 83 ]. Further, this population has experienced long-standing barriers to receiving and participating in mental health services [ 84 ].

The current study provides some support toward the mounting evidence that excessive screen time, including during the pandemic, may negatively impact mental health [ 85 ]. People who manage COVID-19 anxiety with excessive use of smartphones and other screen-based technology inadvertently learn more about the virus from the news, which fuels anxiety and ongoing coping through screens, thus causing a downward spiral [ 82 ]. Excessive use of digital media also detracts from time that could be spent on other health-promoting activities such as outdoor recreation [ 86 ]. Our study supports these relationships, suggesting negative impacts of screen time and positive impacts of "green time" on students' psychological health. The unadjusted analyses suggested that outdoor time predicted psychological impacts of COVID-19, although this variable was not significant in multivariate models. Other studies justify its consideration as a risk factor by university administrators. Both outdoor recreation [ 87 ] and nature exposure [ 88 , 89 ] can improve psychosocial and eudaimonic well-being [ 90 , 91 ]. Recent studies of people across the world show protective psychological effects of park and green space access during the pandemic [ 92 ] as well as lower rates of infection and mortality [ 93 ].

The finding that knowing someone infected is a risk factor for psychological impacts of COVID-19 is intuitive. Familiarity can increase the salience and perceived risk of becoming infected and dealing with subsequent health concerns, like COVID-19-related death [ 79 ]. Also, the threat of death from COVID has been associated with students' mental health and explainable by unhealthy levels of smartphone use [ 82 ].

As suggested in our unadjusted analyses and the multivariate model with the representative sample, SES may influence students' mental health during the pandemic. This might be a result of financial concerns affecting college students and their families [ 10 ]. SES has been documented as a predictor of COVID-19 fear and mental health concerns in other populations [ 10 , 74 , 79 , 94 – 98 ]. Students coming from low-SES families may be more concerned about basic needs, like food and shelter, caused by loss of their or their parent's income [ 99 ]. Furthermore, since low-SES families are more susceptible to COVID-19 infection [ 98 ], students may be more concerned for their own and their families' safety.

4.2 Recommendations for universities

Given the large percentage of students assigned to the high psychological impact profile, universities would be well-served to address the mental health needs of their entire student body. Select programs that have promoted mental health—such as those at the University of Connecticut, University of Kentucky, and Northeastern—include virtual group exercise and meditation/mindfulness sessions, accountability buddies and exercise challenges and tele-medicine/counseling visits [ 99 ]. These group meetings may be helpful not only in lowering anxiety but also in decreasing the sense of isolation reported by the students in this study. Digital interventions for students with clinical levels of anxiety or depression as well as potential for self-harm or suicide can involve automated and blended therapeutic interventions (such as apps and online programs), calls/text messages to reach those with less digital resources, suicide risk assessments, chatlines and forums, and other technologies to monitor risk either passively or actively [ 80 ]. Recently, Chen et al. [ 100 ] recommended a six-step intervention for the reduction in psychological impact risk amongst Chinese college students. These steps included the delivery of positive pandemic-related information, reduction in negative behavior, learning about stress management techniques, improvements in family relationships, increases in positive behavior, and adjustments in academic expectations.

Given the likelihood of ongoing psychological distress from COVID-19, universities may also consider helping students maintain healthy mindsets rather than avoiding stress [ 101 ]. In support of this proposition are recent findings that cognitive and behavioral avoidance (i.e., avoiding situations where exposure is possible and difficult thoughts about the pandemic) was the most consistent predictor of increased anxiety and depressive symptoms during the pandemic [ 17 ]. Cognitive reappraisal of stressful situations can alter their negative impacts [ 102 , 103 ]. Training students to shift their educational experience mind-set to one that focuses on the "silver linings" and emerging opportunities may lead to "stress-related growth" and "toughening" [ 104 , 105 ]. Adaptive mindsets can also help reorganize priorities to develop deeper relationships and greater appreciation of life [ 106 ], as well as help students to adjust to new ways of learning. Since a portion of the students in this study reported feeling less motivated, productive, and able to focus, switching to an adaptive mindset may help students persevere in their education and later in life. Finally, mindset reappraisals can increase well-being, decrease negative health symptoms, and boost physiological functioning under acute stress when a family member becomes infected or the pandemic creates rapid shifts in policies and procedures that affect students [ 107 , 108 ].

Universities can further develop platforms that facilitate safe student social interaction. Many students seek out social interaction during their university experience [ 109 – 111 ]. However, as the findings of this study revealed, students’ opportunities for socializing significantly decreased in the early stages of COVID-19. Missing "going out" and important milestone events (e.g., graduation, last sporting event) was a frequent response from our student participants. Other studies found that in order to maintain students' mental health during the first wave of the COVID-19 pandemic, they communicated online with close family members or roommates at least daily [ 10 ]. With college students, physical distancing does not and should not require "social distancing" [ 101 ]. Both synchronous (i.e., Zoom) and asynchronous (i.e., Facebook group) online interactions can foster bonding and bridging social connection [ 112 – 115 ], which can extend beyond social media posts and email listservs. Normal venues where people congregate such as places of worship, gyms, cafeterias, yoga studios and classes can be replicated online or even held outdoors in temperate weather on a schedule similar to what was in place prior to the pandemic [ 116 ]. Other recently-successful interventions include the facilitated online sharing of recipes, books, and podcasts as well as virtual movie, game, trivia, or happy hour nights [ 99 ]. Providing support to student organizations to coordinate these virtual social activities could accelerate the availability of these resources.

Colleges and universities also have a moral obligation to boost their outreach to particularly vulnerable groups–that is, populations at risk of high levels of psychological impact from COVID-19 [ 14 ]. As documented in the impact profiles of our study, people at increased risk include women, younger students, students with pre-existing health concerns, students spending at least one-third of their day (including time spent sleeping) on screens, and students with family or community members who are infected with COVID-19. Monitoring and reporting rates of anxiety, depression, self-harm, suicide and other mental health issues within these groups is necessary to allocate counseling services and intervene pre-emptively and at times of acute symptomology [ 80 ]. Further, universities can provide accommodations for assignments and exams using a more personalized approach to learning and create enhanced opportunities for virtual social interactions with peers. These efforts may help at-risk groups succeed academically, build stronger relationships, and enhance their sense of belonging during distant learning [ 117 ].

Students in this study also expressed stress and anxiety associated with changes in education mode during the pandemics. As previous research has found, academic success may be supported with virtual town halls, regular email check-ins, virtual office hours, and peer mentoring [ 104 ]. Globally, students' satisfaction with university response to COVID-19 is predicted by students' satisfaction with pre-recorded videos during online course delivery, sufficient information on exams, satisfaction with teaching staff, satisfaction with websites and social media information with regular updates from the university, hopefulness, (lack of) boredom, (lack of) study issues, being on scholarship, being able to pay for school, and study discipline (social sciences tend to be less impacted than hard sciences or engineering) [ 10 ]. Universities may be encouraged by findings from another study on the switch to online courses; this study found many students were not challenged by the transition because of their aptitude toward digital learning and new technologies [ 118 ]. However, another study found new software platforms can be a challenge for some students [ 10 ].

4.3 Strengths and limitations

The primary strength of this study is the development of psychological impact profiles using data from universities across the United States. This sampling approach is also a limitation, however. Whereas all the included universities were teaching exclusively online during the study, their respective states and localities may have experienced differing levels of social distancing policy and enforcement. Another limitation related to the sample is the high percentage of non-Hispanic Whites. This occurrence was likely the result of the demographic composition of the colleges and departments targeted for recruitment [ 119 ]. Selection bias related to which students participated in the study questionnaire based on interest and access/availability is also possible [ 3 ].

Another limitation is the quantitative assessment of the psychological impacts of COVID-19, which could have limited the utility of our impact profiles. We did not measure substance abuse, which is expected to be a ramification of the virus [ 116 ] and which anxious individuals are prone to under-report [ 120 ]. Such counterproductive coping behaviors could be particularly problematic for college students [ 121 ]. Further, because our predictors explained a small amount of variance of the profiles, other unmeasured (or better measured) factors might predict students’ psychological risk. For example, our single-item measures of leisure time activities could be improved with a more comprehensive assessment of time budgets such as those employed in episodic time use surveys [ 122 ].

We were primarily interested in reactions to the pandemic rather than how people were feeling/behaving during the pandemic. Therefore, we did not employ standardized measures of stress, anxiety, depression, or well-being. This limits our findings from being directly compared to other studies and pooled in meta-analyses.

Lastly, our measures were retrospective rather than longitudinal, which decreases our ability to say with confidence that the reported impacts were caused by COVID-19. However, we are fairly confident that the findings are attributable to the pandemic given our survey prompts. They specified students' responses to COVID-19 rather than asked generalized psychological states, and the findings strongly aligned with those of longitudinal studies of college students during the pandemic [ 16 , 17 , 37 , 123 – 125 ].

5 Conclusion

Our cross-sectional study found that being a woman, being of younger age, experiencing poor/fair general health, spending extensive time on screens, and knowing someone infected with COVID-19 were risk factors for higher levels of psychological impact during the pandemic among college students in the United States. Unadjusted analyses also suggested that students who were non-Hispanic White, were not non-Hispanic Asian, were of higher-SES, or spent at least two hours outside experienced lower levels of psychological impact. That said, all students surveyed reported being negatively affected by the pandemic in some way, and 59% of respondents experienced high levels of psychological impact.

At the time that these data were collected, the education of over 1.5 billion students across the world were affected by COVID-19 [ 126 ]. Rates of student psychological distress were as high as 90% [ 17 , 127 ]. Students must "Maslow before they can Bloom; " in other words, their basic physiological, psychological, and safety needs must be met prior to them focusing on–much less excelling–in academic life [ 99 ]. We recommend that university administrators take aggressive, proactive steps to support the mental health and educational success of their students at all times, but particularly during times of uncertainty and crisis–notably, the COVID-19 pandemic.

Supporting information

S1 fig. distributions and relationships between covid-19 psychological impact survey items, including histograms, pearson correlation coefficients, and scatter plots..

https://doi.org/10.1371/journal.pone.0245327.s001

S2 Fig. Diagram of EFA on COVID-19 psychological impact survey items.

https://doi.org/10.1371/journal.pone.0245327.s002

S3 Fig. Scree plot of EFA on COVID-19 psychological impact survey items.

https://doi.org/10.1371/journal.pone.0245327.s003

S4 Fig. Elbow plot of the information criteria for the latent profile analysis.

https://doi.org/10.1371/journal.pone.0245327.s004

S5 Fig. Correlations between socio-economic measures and the two psychological impact profiles.

https://doi.org/10.1371/journal.pone.0245327.s005

S6 Fig. Conditional mean values ("condval") and standard deviations of institutional affiliation (university) random effects from mixed-effects logistic regression predicting high versus medium/low psychological impact profile from COVID-19.

https://doi.org/10.1371/journal.pone.0245327.s006

S1 Table. Item loadings and fit statistics of EFA on COVID-19 psychological impact survey items.

https://doi.org/10.1371/journal.pone.0245327.s007

S2 Table. Fit indices, entropy and model comparisons for estimated latent profile analyses models.

https://doi.org/10.1371/journal.pone.0245327.s008

S3 Table. Results of binomial logistic regression modelling likelihood of risk factors predicting high versus low/moderate levels of COVID-19 psychological impact for students at North Carolina State University, where a representative sample was collected ( N = 1,312).

https://doi.org/10.1371/journal.pone.0245327.s009

https://doi.org/10.1371/journal.pone.0245327.s010

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Classroom Q&A

With larry ferlazzo.

In this EdWeek blog, an experiment in knowledge-gathering, Ferlazzo will address readers’ questions on classroom management, ELL instruction, lesson planning, and other issues facing teachers. Send your questions to [email protected]. Read more from this blog.

Students Share How COVID Has Changed Their Lives

covid 19 essay for students

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(This is the final post in a two-part series. You can see Part One here .)

The new questions-of-the-week (directed toward students) is:

What is the best thing about school this year? Why?

What is the worst thing about school this year? Why?

Several students from our school shared their responses to these questions in Part One .

Here are even more:

Disrupted Plans

Pachia Xiong is a junior at Luther Burbank High School in Sacramento, Calif.:

The best thing about school this year is being able to hang out with my friends again. I think this is the best thing for me this year because I don’t have to use social media or Zoom to contact them. As the previous school year was spent through distance learning, I had little contact with my friends because I was not someone who frequented my social media back then. Now, I am able to, with ease, talk to them in person. It feels much nicer that way.

The worst thing about school this year is being unable to do everything I planned because of COVID-19. Over last year, during distance learning, I was highly hopeful of returning to school in person. Following those hopes came the goals I wanted to achieve upon our return. Some of which involved holding club events and activities that could be enjoyed by both club members and outsiders. However, the rise in COVID-19 cases put a lid over those goals, and now, everything feels as though it’s come to a stop.

noweverythingpachia

‘Classes Are Easier’

Brenda Lin is a senior at Luther Burbank High School:

The best thing about this year is that most of my classes are easier because of the pandemic. For example, most of my finals for first semester were really easy because a lot of it was stress-free assignments. The teachers are more understanding about missing work and absences, and overall, I get sick less when I’m at school because I’m wearing a mask all the time. Overall, it’s a good way to end my high school years.

The worst thing about this year is that we have to be in school during the pandemic. When we were in distance learning, it was definitely a lot easier in terms of difficulty of assignments, but my motivation was very low. It is about the same in in-person school, but now, I have to worry about getting sick and bringing it back to all my family members. Not only that, but some of my teachers assign difficult assignments and require a lot of time and work to complete, and it’s not something I’m willing to do.

teachersaremorebrenda

‘Being Able to Learn New Things’

Abby Funez is a senior at Luther Burbank High School:

The best thing about school this year is being able to learn new things every day that benefit me later on in life. This is the best thing because I am able to grow in knowledge and mature a lot more. For example, I am learning how to write essays while being timed and under pressure. This will help me in my admissions for classes in college and to be a better writer, which will benefit me long term throughout my life.

The worst thing about school this year is wearing masks. This is the worst thing because it makes it harder to hear our teachers and students within our area. In my Spanish class, my teacher is unable to hear other students even when they are standing by her because of the masks. This makes my life difficult because I miss important concepts of the lesson being taught at times.

theworstthingabby

‘Teachers Are More Lenient’

Julianna Eakle is a junior at Luther Burbank High School:

The best thing about this school year is how the teachers are more lenient of our absences or our missing assignments. For example, our principal put out an email saying that he understands if parents would like to keep us home due to our safety, for students just to continue doing our work online. My teachers were very concerned about me not attending class but did everything to help me stay on top of my grades.

The worst thing about this school year is people not having their masks over their nose and mouths. There are at least 3 teachers a day telling one of their students to put their mask over their nose and mouth. It’s a serious problem, and because of that one person, COVID cases start to spike fast.

theworstthingjuliana

‘We Can’t Eat or Drink Water in Class’

Van Bui is a senior at Luther Burbank High School:

The best thing about school this year is meeting new people and joining different sports like volleyball and track and field. This is the best thing because it taught me to enjoy life and take risks as I go. For example, I get to know a lot of people and hang out with them to do fun activities with, and joining different sports allowed me to step out of my boundaries and improve my health especially during these times. This makes my life better because I was always scared to talk to people and do different sports.

The worst thing about school this year is having COVID-19 going on still. This is the worst thing because I always have to wear a mask and there are limited activities that we can do in school. For example, we can’t eat or drink water in class, waking up early in the morning, and before getting fresh air, it’s blocked by wearing a mask for 8 hours straight. This makes my life worse because I find it very difficult to breathe or eat in class.

thebestthingvan

‘We Are Back in Person’

Lakeyah Roots is a junior at Luther Burbank High School:

The best thing about school this year is being able to learn in person. When we were doing distance learning, being able to learn the information being taught to me and my ability to do my schoolwork was not good. But now that we are back in person, I feel like I can do more work more efficiently and really get the help I need. Distance learning has taught me that doing school online does not suit me.

The worst thing about school is COVID cases. Students were getting sick, and that caused the class to be empty sometimes. The classroom does not feel the same when it is not filled with the students you normally see every day. It is not fun not being able to do certain activities because of COVID. It’s best to keep our distance from one another, but sometimes I miss the days when we were able to do certain class activities before COVID hit.

imissthedayslake

Seeing Friends

Joanna Medrano-Gutierrez is a junior at Luther Burbank High School:

The best thing about this school year is being able to see my friends again. This is the best thing about this school year because I haven’t seen most of them since the pandemic started. For example, I haven’t seen a certain friend since March 2020, but now, this school year, we are closer than we were before.

The worst thing about this school year is adapting back into waking up early again. This is the worst thing about the pandemic because I got so used to sleeping late and sleeping in, and then I had to get used to waking up early. For example, before I woke up at 9 a.m.-12 p.m., but now I wake up at 6 a.m.-7 a.m.

theworsthingjoanna

Thanks to Pachia, Brenda, Abby, Julianna, Van, Lakeyah, and Joanna for contributing their thoughts.

Consider contributing a question to be answered in a future post. You can send one to me at [email protected] . When you send it in, let me know if I can use your real name if it’s selected or if you’d prefer remaining anonymous and have a pseudonym in mind.

You can also contact me on Twitter at @Larryferlazzo .

Education Week has published a collection of posts from this blog, along with new material, in an e-book form. It’s titled Classroom Management Q&As: Expert Strategies for Teaching .

Just a reminder; you can subscribe and receive updates from this blog via email (The RSS feed for this blog, and for all Ed Week articles, has been changed by the new redesign—new ones are not yet available). And if you missed any of the highlights from the first 10 years of this blog, you can see a categorized list below.

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

Giorgio di pietro.

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

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

Associated Data

Data will be made available on request.

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

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

1. Introduction

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

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

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

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

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

2.1. Inclusion criteria

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

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

2.2. Search ad screening procedures

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

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

Fig. 1

Flow chart of the search and screening process.

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

2.3. Study coding

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

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

2.3.1. Effect size calculation

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

2.3.2. Moderator variables

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

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

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

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

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

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

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

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

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

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

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

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

Coming to 2) characteristics, we code:

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

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

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

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

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

2.4. Sample characteristics

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

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

Sources for meta-analysis.

Study (Author(s) and year of publication)CountryType of test used to measure student performanceNumber of effect sizes collectedMean effect size
South AfricaIndividualstudent assessment administered by fieldworkers4−0.42
SpainRegional competency-based assessments4−0.04
USAdaptive assessment (FastBridge)16−0.14
DenmarkNationwide standardised tests50.05
ItalyNationwide standardised tests4−0.04
ChinaStandardised tests10.22
ItalyStandardised tests2−0.21
ItalyLocal assessment at a single institution1−0.11
GermanyRegional standardised tests32−0.01
USOnline reading assessment tool (Literably)4−0.03
EgyptLocal assessment at a single institution1−0.13
NetherlandsStandardised tests4−0.08
SwedenOnline assessment tool (LegiLexi)180.09
ChinaLarge-scale exams administered by local governments8−0.50
BelgiumStandardised tests in the Flemish region22−0.13
AustraliaProgressive achievement tests administered by trained research assistants40.04
NetherlandsStandardised tests3−0.12
MexicoIndependent Assessment of Learning (MIA)2−0.54
USLocal assessment at a single institution5−0.22
USState assessment1−0.23
USState assessment11−0.23
Czech RepublicIdentical tests on a panel of 88 schools from all regions2−0.08
USComputer adaptive test (MAP Growth)12−0.10
USComputer adaptive test (MAP Growth)24−0.12
BrasilStandardised tests in the São Paulo State3−0.31
GermanyProgress in International Reading Literacy Study2−0.17
BelgiumStandardised tests in the Flemish region11−0.16
NetherlandsDigital learning assessment tool (Snappet)100.15
SpainLocal assessment at a single institution1−2.34
SpainLocal assessment at a single high school1−0.95
USAssessment of the same course across 4 institutions2−0.12
UKNFER assessments6−0.17
GermanyRegional mandatory standardised tests3−0.06
Netherlandsnationally standardised tests2−0.08
NorwayTest administered by students at a single school2−0.48
GermanyAssessment from an online mathematics platform (Bettermarks)20.15
SwitzerlandAdaptive computer-based tool for formative student assessment (MINDSTEPS)2−0.07
NetherlandsAssessment from an online retrieval practice tool used for language learning10.25
SloveniaLocal assessment at a single institution10.11

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

Descriptive statistics.

Variable nameUnweighted Mean (Standard deviation) (1)Weighted (by the inverse of the number of estimates reported in each study) Mean (Standard deviation) (2)Number of effect sizes (3)
Effect size (Cohen's )−0.112 (0.271)−0.187 (0.436)239
Effect size's standard error0.021 (0.030)0.035 (0.048)239
Math/Science0.423 (0.495)0.401 (0.491)101
Humanities0.527 (0.500)0.456 (0.499)126
Mix0.050 (0.219)0.143 (0.351)12
Primary0.615 (0.488)0.514 (0.501)147
Secondary0.343 (0.476)0.359 (0.481)82
Tertiary0.042 (0.201)0.127 (0.334)10
20200.657 (0.476)0.676 (0.469)157
20210.343 (0.476)0.324 (0.469)82
Europe0.590 (0.493)0.610 (0.489)141
Non-Europe0.410 (0.493)0.390 (0.494)98
20200.126 (0.332)0.127 (0.334)30
20210.702 (0.458)0.644 (0.480)168
20220.172 (0.378)0.229 (0.421)41
Longitudinal0.339 (0.474)0.339 (0.474)81
Cross-sectional0.661 (0.474)0.661 (0.474)158
Descriptive0.130 (0.337)0.242 (0.429)31
Correlational0.765 (0.424)0.555 (0.498)183
Quasi experimental/experimental0.105 (0.307)0.203 (0.403)25
Journal article0.247 (0.432)0.458 (0.499)59
Other publication0.753 (0.432)0.542 (0.499)180

2.5. Risk of bias assessment

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

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

Risk of bias domain: ROBINS-I.

StudyBias due to confoundingBias in participant selectionBias in classification of interventionsBias because of missing dataBias in measurement of outcomesBias in selection of the reported resultOverall risk of bias
moderatemoderatelowmoderatelowlowmoderate
Lowlowlowlowlowlowlow
moderatemoderatelowlowlowlowmoderate
Lowlowlowlowlowlowlow
Lowlowlowmoderatelowlowmoderate
seriousseriouslowmoderatelowlowserious
moderatemoderatelowmoderatelowlowmoderate
lowlowlowlowlowlowlow
seriouslowlowmoderatemoderatelowserious
moderatemoderatelowmoderatelowlowmoderate
seriousmoderatelowseriouslowlowserious
lowlowlowlowlowlowlow
seriousmoderatelowseriouslowlowserious
seriousseriouslowseriouslowlowserious
lowmoderatelowmoderatemoderatelowmoderate
moderatelowlowlowlowmoderatemoderate
lowlowlowmoderatelowlowmoderate
seriousseriouslowmoderatelowlowserious
moderatemoderatelowmoderatelowlowmoderate
lowmoderatelowmoderatelowlowmoderate
seriouslowlowseriouslowlowserious
moderatelowlowmoderatelowlowmoderate
moderatelowlowmoderatelowlowmoderate
lowlowlowlowlowlowlow
moderatelowlowlowlowlowmoderate
lowlowlowlowlowlowlow
seriousmoderatemoderatelowlowmoderateserious
seriousmoderatelowSeriousmoderatelowserious
seriousmoderatelowSeriousmoderatelowserious
moderatelowlowmoderatelowlowmoderate
seriousseriouslowN/Alowlowserious
moderatemoderatemoderatemoderatelowlowmoderate
seriousmoderatelowSeriouslowlowserious
moderatelowlowmoderatemoderatelowmoderate
seriouslowlowLowlowmoderateserious
seriouslowlowmoderatelowlowserious
seriousmoderatelowLowmoderatelowserious
moderatemoderatelowmoderateseriouslowserious

2.6. Estimators and models

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

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

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

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

2.7. Publication bias

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

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

Fig. 2

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

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

3. Results and discussion

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

3.1. Summary effect size

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

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

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

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

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

3.2. Heterogeneity

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

Meta-regression results.

General model (1)Specific model (2)Robust Specific model (3)Robust Specific model (using the inverse of the variance as weight) (4)
Constant−0.119 (0.175)−0.173*** (0.049)−0.173*** (0.032) [0.000]−0.207*** (0.055) [0.051]
Math/Science−0.170*** (0.008)−0.170*** (0.007)−0.170*** (0.008) [0.000]−0.180*** (0.000) [0.000]
Mix−0.113 (0.144)
Secondary0.097*** (0.008)0.097*** (0.008)0.097*** (0.007) [0.298]0.102*** (0.000) [0.334]
Tertiary0.142 (0.292)
20210.080 (0.066)
Europe0.180*** (0.068)0.193*** (0.051)0.193*** (0.034) [0.002]0.244*** (0.055) [0.013]
20200.013 (0.058)
2021−0.032 (0.095)
(
Longitudinal0.079 (0.109)0.141*** (0.049)0.141*** (0.032) [0.020]0.178*** (0.055) [0.153]
(Reference category: descriptive)
Correlational−0.085 (0.131)
Quasi experimental/experimental−0.223 (0.170)−0.228*** (0.050)−0.228*** (0.029) [0.002]−0.205*** (0.055) [0.005]
Journal article−0.110** (0.044)−0.097** (0.041)−0.097*** (0.018) [0.235]−0.143*** (0.005) [0.646]
Standard Error−0.194 (2.834)
R-squared0.7470.7420.7420.792
No. observations239239239239

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

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

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

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

Bayesian model averaging (BMA).

BMA
Post meanPost St. errorPIP
Constant−0.0590.1061.00
−0.1500.0091.00
Mix−0.1370.1520.50
Secondary0.5350.9800.29
Tertiary0.0090.0980.07
2021 (Timing of student assessment during Covid-19)0.0110.0370.13
0.0740.0920.73
2020 (Year of publication)0.0100.0360.17
2021 (Year of publication)−0.0070.0420.16
Longitudinal0.0030.0550.22
Correlational0.0210.0730.15
Quasi experimental/experimental−0.1100.1390.44
−0.1020.0910.64
Standard Error−0.1241.0700.07

3.3. Discussion of the main results

Our meta-analysis delivers six main results.

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

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

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

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

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

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

4. Conclusions

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

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

Funding details

This work has not been supported by any grants.

CRediT authorship contribution statement

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

Declaration of competing interest

No potential conflict of interest was reported by the author.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Data availability

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  • Education: From disruption to recovery. UNESCO. (2021, November 7). Retrieved November 14, 2021, from https:en.unesco.orgcovid19educationresponse.
  • Centers for Disease Control and Prevention. (n.d.). Guidance for covid-19 prevention in K-12 Schools. Centers for Disease Control and Prevention. Retrieved November 14, 2021, from https:www.cdc.govcoronavirus2019-ncovcommunityschools-childcarek-12-guidance.html.

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“Long Covid feels like a gun to my head,” by Rachel Hall-Clifford

As someone living with chronic illness, I just want to a) applaud the author and everyone else out there who continues surviving and fighting for answers about long Covid and other post-viral syndromes and b) want to provide a bit of a public service announcement:

It’s well known amongst the community of people living with postural orthostatic tachycardia syndrome (POTS) at this juncture that long Covid is largely a trauma/virus induced dysfunction of the autonomic nervous system (aka dysautonomia), specifically POTS. Many of us have lived with the symptoms of “long Covid” long before there was Covid. Folks genetically predisposed to autoimmunity and other precursors to POTS were extremely likely triggered by the coronavirus. It pains me that this is still not common knowledge for sufferers. Please seek out help from a POTS specialist and continue digging into your underlying condition, when you have the energy, so that you can eventually regain a fuller life. It’s not easy and takes a tremendous amount of time and will. But it will be worth it. Be as well as possible!

— Sandra Ivanov

“FDA: Don’t rush publishing your diversity guidance plan. Take your time and do it right,” by Tamei Elliott and Maria Vassileva

“Equity” in clinical trial participation doesn’t mean that trials “look like America,” but rather that they “look like the therapeutic population.” But it’s got to be more than just about clinical trial participants. What’s equally important is that we must also expand diversity in clinical trial designers, recruiters, principal investigators, FDA review teams, and advisory committee members — and not just patient representatives. This isn’t the end, it is only the beginning, and the goal mustn’t be diversity for diversity’s sake, but to facilitate better trials leading to better data, better agency reviews, better and more precise labeling, resulting in and better patient options and outcomes.

— Peter Pitts, Center for Medicine in the Public Interest

“AI and rural health care: A paradigm shift in America’s heartland,” by Bill Gassen

I found some of AI’s potential cures misleading. While the article states AI does not save clinician time reducing cognitive burden, the burden of responding to patients is not lifted by text prompts. And those fully transcribed clinical encounters have to be fully reviewed. Without knowing the why of higher rates of later-stage cancers, risk calculators and reminders may not deliver on their supposed promise. Much of what AI promises is to repair the unintended consequences of the last great idea, electronic health records.

Can AI make inroads into the disparities of care for our rural citizens? Perhaps. But this, like many other articles, is more about vested interests looking at the newest shiny object that promises to “move fast, break things, and apologize later.”

— Charles Dinerstein, American Council on Science and Health

About the Author Reprints

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Acting First Opinion Editor

Patrick Skerrett is filling in as editor of First Opinion , STAT's platform for perspective and opinion on the life sciences writ large, and host of the First Opinion Podcast .

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IMAGES

  1. Fourth Grader Pens Essay About Coronavirus Anger and Fears

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  2. How To Write About Coronavirus In Your College Essays

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  3. 📗 Impact on International Students During the Covid-19

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  4. Protecting and mobilizing youth in COVID-19 responses

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  5. Complete Essay on Coronavirus (COVID-19) (with latest statistics)

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  6. 📗 Essay Sample on Impact of COVID 19

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VIDEO

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COMMENTS

  1. Writing About COVID-19 in Your College Essay

    This essay is an opportunity to share your pandemic experience and the lessons learned. The college admissions process has experienced significant changes as a result of COVID-19, creating new challenges for high school students. Since the onset of the pandemic, admissions officers have strongly emphasized a more holistic review process.

  2. How to Write About COVID-19 In Your College Essay & Application

    This year, the Common App is including a special 250-word section allowing students to describe the impacts of COVID-19 on their lives. Here's the official word from the Common App website: . We want to provide colleges with the information they need, with the goal of having students answer COVID-19 questions only once while using the rest of the application as they would have before to ...

  3. How to Write About Coronavirus in a College Essay

    Writing About COVID-19 in College Essays. Experts say students should be honest and not limit themselves to merely their experiences with the pandemic. The global impact of COVID-19, the disease ...

  4. What Life Was Like for Students in the Pandemic Year

    In these short essays below, teacher Claire Marie Grogan's 11th grade students at Oceanside High School on Long Island, N.Y., describe their pandemic experiences. ... My mom had COVID-19 for ten ...

  5. Positive Impacts of COVID-19

    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 ...

  6. Writing about COVID-19 in a college essay GreatSchools.org

    Students working on college admission essays often struggle to figure out how to write about their experiences during the COVID-19 pandemic. For students applying to college using the CommonApp, there are several different places where students and counselors can address the pandemic's impact. The different sections have differing goals.

  7. PDF The Impact of Covid-19 on Student Experiences and Expectations ...

    variation in the e ects of COVID-19 across students. In terms of labor market expectations, on average, students foresee a 13 percentage points decrease in. the probability of. on, a reduction of 2 percent in their reservation wages, a. d a2.3 percent decrease in their expected earn. ID-19 demonstrate that stude.

  8. How is COVID-19 affecting student learning?

    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 ...

  9. PDF My COVID-19 Perspective

    My COVID-19 Perspective Hi there, my name is Jack Gardner, a 5th year student at Purdue University. The COVID-19 pandemic is truly what we describe as a "Black Swan" event, meaning that no-one could have predicted it happening, but the impact it has on society is profound. ... change forever. Today, I am writing this short reflective essay ...

  10. Covid-19's Impact on Students' Academic and Mental Well-Being

    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 ...

  11. The pandemic has had devastating impacts on learning. What ...

    As we outline in our new research study released in January, the cumulative impact of the COVID-19 pandemic on students' academic achievement has been large. We tracked changes in math and ...

  12. Covid 19 Essays: Examples, Topics, & Outlines

    Here are some essay topic ideas related to Covid-19: 1. The impact of Covid-19 on mental health: Discuss how the pandemic has affected individuals' mental well-being and explore potential solutions for addressing mental health challenges during this time. 2.

  13. My Experience as a College Student During COVID-19

    My first two thoughts were mixtures of empathetic concern and selfish relief— "I'm glad I did my study abroad in the fall" and "It must be really tough to be a college senior this year ...

  14. How to Write About the Impact of the Coronavirus in a College Essay

    Writing About Coronavirus in Main and Supplemental Essays. Students can choose to write a full-length college essay on the coronavirus or summarize their experience in a shorter form. To help ...

  15. The impact of COVID-19 on student experiences and expectations

    Our findings on academic outcomes indicate that COVID-19 has led to a large number of students delaying graduation (13%), withdrawing from classes (11%), and intending to change majors (12%). Moreover, approximately 50% of our sample separately reported a decrease in study hours and in their academic performance.

  16. Coronavirus: My Experience During the Pandemic

    The coronavirus is a virus that originated in China, reached the U.S. and eventually spread all over the world by January of 2020. The common symptoms of the virus include shortness of breath, chills, sore throat, headache, loss of taste and smell, runny nose, vomiting and nausea. As it has been established, it might take up to 14 days for the ...

  17. The impact of COVID-19 on student achievement and what it ...

    The two figures below show projected math and reading learning patterns from the beginning of the 2019-20 school year (before COVID-19 school closures) through the start of the 2020-21 school year.

  18. Essay: COVID-19 and humanity's interconnectedness

    Becoming a storyteller at WHYY, your local public media station, is easier than you might think. Text STORYTELLER to 267-494-9949 to learn more. WHYY is your source for fact-based, in-depth journalism and information. As a nonprofit organization, we rely on financial support from readers like you. Please give today.

  19. Why lockdown and distance learning during the COVID-19 ...

    The COVID-19 pandemic led to school closures and distance learning that are likely to exacerbate social class academic disparities. This Review presents an agenda for future research and outlines ...

  20. Psychological impacts from COVID-19 among university students: Risk

    Background University students are increasingly recognized as a vulnerable population, suffering from higher levels of anxiety, depression, substance abuse, and disordered eating compared to the general population. Therefore, when the nature of their educational experience radically changes—such as sheltering in place during the COVID-19 pandemic—the burden on the mental health of this ...

  21. Students Share How COVID Has Changed Their Lives (Opinion)

    Seeing friends and getting more leniency from teachers are two things students like about school this year. Waking up early is not, though. ... Submit an Essay ... the rise in COVID-19 cases put a ...

  22. The impact of Covid-19 on student achievement: Evidence from a recent

    This work attempts to synthetize existing research about the impact of Covid-19 school closure on student achievement. It extends previous systematic reviews and meta-analyses by (a) using a more balanced sample in terms of country composition, (b) considering new moderators (type of data and research design), and (c) including studies on tertiary education students in addition to primary and ...

  23. Going Back to School after Covid-19: Narrative Essay

    The Covid 19 pandemic has affected many aspects of school life, all in order to prevent any further spread of the disease. Our school is working hard to go back to the normal school life we used to have before the global pandemic. Yet, parents are still worried about their kids, and teachers about their students.

  24. UNESCO

    This question is for testing whether you are a human visitor and to prevent automated spam submission. Audio is not supported in your browser.

  25. First Opinion readers respond to essays on long Covid and more

    First Opinion is STAT's platform for interesting, illuminating, and maybe even provocative articles about the life sciences writ large, written by biotech insiders, health care workers ...