covid 19 case study for grade 5

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The evidence base, evidence of covid-19’s impact on k-12 education points to critical areas of intervention.

At the start of the COVID-19 pandemic, researchers at USC Dornsife Center for Economic and Social Research (CESR) began tracking social, economic, and education outcomes among Americans through its nationally-representative online panel, the Understanding America Study (UAS) with funding from the Bill and Melinda Gates Foundation and the National Science Foundation. Between April and October 2020 we administered five rounds of questions to approximately 1400 households with at least one child in Kindergarten-12 th grade, asking about COVID-19’s effects on K-12 education. We collected five waves of data from these same parents between April and October 2020, and we will continue to administer questions over the coming months.

Below are the key findings we have found thus far.

At the Beginning of the Pandemic, We Found Large Disparities in Educational Experiences

In April 2020, only about two-thirds of households with income less than $25,000/year had computers and internet access available for children’s remote learning, compared to 91% of families with household incomes of $75,000-$149,000, and 98% of those above $150,000.

We also found large disparities by income group in the proportion of families reporting their children participating in even the most basic of educational activities. While most parents reported their children were receiving work from their child’s school , lower proportions were interacting with teachers (online or by phone) or receiving feedback, with stark differences by income. Among highest-income households, 89% reported interaction with teachers compared to 57% in lowest-income households. Similarly, 72% of highest-income households reported feedback from a teacher compared to 57% among lowest-income families.

We also saw that the rates of students receiving critical services, like free and reduced-priced meals and special education services, dropped dramatically following school closures. Prior to COVID-19-related school closings, roughly 40% of responding parents reported their children were receiving free or reduced-price meals at school. By April, after school closures, this number dropped by almost half, to 21%. Similarly, 11% of parents reported their child was receiving special education services or accommodations through an IEP or 504 plan pre-pandemic. By April, this number had dropped to just 7% still receiving special education services.

Percentage of Children Receiving Free/Reduced-Price School Lunches Dropped By Nearly 50% After Spring 2020 School Closures

In the Summer, Parents Shared Their Preferences for the Fall

Over the summer, a proposed policy of remote-only learning for the 2020-21 school year enjoyed support from a minority of parents, 43% overall. UAS data was the first to shed light on the initially unexpected result—subsequently played out in districts nationwide—that parents of color and lower-income families would be less likely to send their children to school in person even when the option was available.

Large Differences by Race in Preferences for Not Sending Children Back to School in Person

The richness of the UAS dataset, collecting broad information from these same households over time, demonstrated that families of color and low-income families were also the most frightened of hospitalization and death from COVID-19, perhaps partially explaining the finding that higher proportions of these families supported remote-only instruction in the fall. Parents also shared their desire for fall improvements, including additional time spent on synchronous or live instruction, and an increased amount or frequency of teacher feedback.

By October 2020, Parents of Fully in-Person Learners Reported Improvements. But There’s a Long Way to Go for Remote Learners

In October, nearly 70% of children were learning fully or partially remotely— with rates differing by many factors (e.g. by region of the country, race/ethnicity, household income, parental educational attainment, partisanship).

% of Households with Children Attending School Fully or Partially Remotely

Nearly 40% of parents who reported their children needed tutoring said their schools did not provide it. Of parents with no more than a high school degree, half felt equipped to help their own children with homework. For example, 51% of parents with a high school degree or less felt comfortable helping their children with math homework. And while computer/device provision had improved over the spring, 3.5% of remote learners still did not have a device for learning—and nearly one in ten reported sharing a device (more so for children from low-income households). In addition, while just over one percent of families did not have internet access, 22% reported continuous internet interruptions, 40% for the lowest income.

We also found more than one in ten families with remote learners had formed “pods” where students learned together in-person with the help of a tutor or teacher. Somewhat unexpected given concerns shared broadly in the media during the summer that more privileged families’ use of pods would exacerbate already immense existing educational inequality, the proportion of families engaged in pods was 15% higher among families with household income less than $50K. We do not know if these pods are being used primarily for education or child care purposes, or the types of adults who are staffing these pods and whether that differs by demographic group.

In October we found schools were delivering more services than in April, but not at pre-COVID-19 levels. Meal service receipt, which dropped from 40% prior to COVID-19 closures to 20% of households in April, rebounded partially to 30% in October. For special education students (IEP or 504), about 11% of our sample were receiving services in February. This dropped to 7% in April and only rebounded to 8% in October.

We also found that while overall parents’ perceptions of their children’s school quality had improved by October over April, the trend was driven by parents with children back in school in-person . Parents of remote learners “graded” remote learning as less engaging, and of lower quality across all content areas. In addition, more families of remote-learners became concerned about children’s emotional health, while there was no change or a small decrease in the number of parents concerned among in-person or hybrid students.

Parents’ Grades for School’s Overall Quality of Education

Data shows parents’ increasing support for canceling spring 2021 standardized tests over the course of the year. The proportion of parents supporting cancellation rose steadily from 43% in mid-April to 49% in late May to 59% in mid-July to 64% in mid-October, with the consistency of support across demographic subgroups—between 55% and 70% of parents across racial and ethnic, socioeconomic, political, and mode of school attendance groups support canceling standardized testing this coming spring.

Finally, UAS data shows support for wearing face coverings in schools has increased substantially since the summer, from an average of 45% of households supporting a policy of opening schools and requiring students to wear face coverings in July, to 69% of households supporting the same policy in October.

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The COVID-19 impact on reading achievement growth of Grade 3-5 students in a U.S. urban school district: variation across student characteristics and instructional modalities

Affiliations.

1 College of Education, North Carolina State University, Raleigh, NC USA.
2 American Institute for Research, Arlington, VA USA.
3 Harvard Graduate School of Education, Harvard University, Cambridge, MA USA.
PMID: 36406628
PMCID: PMC9662133
DOI: 10.1007/s11145-022-10387-y

The current study aimed to explore the COVID-19 impact on reading achievement growth by Grade 3-5 students in a large urban school district in the U.S. and whether the impact differed by students' demographic characteristics and instructional modality. Specifically, using administrative data from the school district, we investigated to what extent students made gains in reading during the 2020-2021 school year relative to the pre-COVID-19 typical school year in 2018-2019. We further examined whether the effects of students' instructional modality on reading growth varied by demographic characteristics. Overall, students had lower average reading achievement gains over the 9-month 2020-2021 school year than the 2018-2019 school year with a learning loss effect size of 0.54, 0.27, and 0.28 standard deviation unit for Grade 3, 4, and 5, respectively. Substantially reduced reading gains were observed from Grade 3 students, students from high-poverty backgrounds, English learners, and students with disabilities. Additionally, findings indicate that among students with similar demographic characteristics, higher-achieving students tended to choose the fully remote instruction option, while lower-achieving students appeared to opt for in-person instruction at the beginning of the 2020-2021 school year. However, students who received in-person instruction most likely demonstrated continuous growth in reading over the school year, whereas initially higher-achieving students who received remote instruction showed stagnation or decline, particularly in the spring 2021 semester. Our findings support the notion that in-person schooling during the pandemic may serve as an equalizer for lower-achieving students, particularly from historically marginalized or vulnerable student populations.

Keywords: COVID-19; Instructional modality; Reading achievement.

© The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Coronavirus disease (COVID-19): Schools

So far, data suggests that children under the age of 18 years represent about 8.5% of reported cases, with relatively few deaths compared to other age groups and usually mild disease. However, cases of critical illness have been reported. As with adults, pre-existing medical conditions have been suggested as a risk factor for severe disease and intensive care admission in children.

Further studies are underway to assess the risk of infection in children and to better understand transmission in this age group.

The role of children in transmission is not yet fully understood. To date, few outbreaks involving children or schools have been reported. However, the small number of outbreaks reported among teaching or associated staff to date suggests that spread of COVID-19 within educational settings may be limited.

As children generally have milder illness and fewer symptoms, cases may sometimes go unnoticed. Importantly, early data from studies suggest that infection rates among teenagers may be higher than in younger children.

Considering that many countries are starting to slowly lift restrictions on activities, the longer-term effects of keeping schools open on community transmission are yet to be evaluated. Some modelling studies suggest that school re-opening might have a small effect on wider transmission in the community, but this is not well understood. Further studies are underway on the role of children in transmission in and outside of educational settings. WHO is collaborating with scientists around the world to develop protocols that countries can use to study COVID-19 transmission in educational institutions. Click here to access this information .

Whether a child should go to school depends on their health condition, the current transmission of COVID-19 within their community, and the protective measures the school and community have in place to reduce the risk of COVID-19 transmission. While current evidence suggests that the risk of severe disease for children is lower overall than for adults, special precautions can be taken to minimize the risk of infection among children, and the benefits of returning to school should also be considered.

Current evidence suggests that people with underlying conditions such as chronic respiratory illness including asthma (moderate-to-severe), obesity, diabetes or cancer, are at higher risk of developing severe disease and death than people without other health conditions. This also appears to be the case for children, but more information is still needed.

Adults 60 years and older and people with underlying health conditions are at higher risk for severe disease and death. The decision to return to a teaching environment depends on the individual and should include consideration of local disease trends, as well as the measures being put in place in schools to prevent further spread.

The incubation period for children is the same as in adults. The time between exposure to COVID-19 and when symptoms start is commonly around 5 to 6 days, and ranges from 1 to 14 days.

Deciding to close, partially close or reopen schools should be guided by a risk-based approach, to maximize the educational, well-being and health benefit for students, teachers, staff, and the wider community, and help prevent a new outbreak of COVID-19 in the community.

Several elements should be assessed in deciding to re-open schools or keep them open:

The epidemiology of COVID-19 at the local level: This may vary from one place to another within a country
Transmission intensity in the area where the school operates: No cases, sporadic transmission; clusters transmission or community transmission
Overall impact of school closures on education, general health and wellbeing; and on vulnerable and marginalized populations (e.g. girls, displaced or disabled)
Effectiveness of remote learning strategies
Detection and response: Are the local health authorities able to act quickly?
The capacity of schools/educational institutions to operate safely
Collaboration and coordination: Is the school collaborating with local public health authorities?
The range of other public health measures being implemented outside school

School closures have clear negative impacts on child health, education and development, family income and the overall economy.

The decision to reopen schools should include consideration of the following benefits:

Allowing students to complete their studies and continue to the next level
Essential services, access to nutrition, child welfare, such as preventing violence against children
Social and psychological well-being
Access to reliable information on how to keep themselves and others safe
Reducing the risk of non-return to school
Benefit to society, such as allowing parents to work

There are several actions and requirements that should be reviewed and put in place to prevent the introduction and spread of COVID-19 in schools and into the community; and to ensure the safety of children and school staff while at school. Special provisions should be considered for early childhood development, higher learning institutions, residential schools or specialized institutions.

WHO recommends the following:

Community-level measures: Carry out early detection, testing, contact tracing and quarantine of contacts; investigate clusters; ensure physical distancing, hand and hygiene practices and age-appropriate mask use; shield vulnerable groups. Community-led initiatives such as addressing misleading rumors also play an important role in reducing the risk of infection.

Policy, practice and infrastructure : Ensure the necessary resources, policies and infrastructure, are in place that protect the health and safety of all school personnel, including people at higher risk.

Behavioral aspects : Consider the age and capacity of students to understand and respect measures put in place. Younger children may find it more difficult to adhere to physical distancing or the appropriate use of masks.

Safety and security : School closure or re-opening may affect the safety and security of students and the most vulnerable children may require special attention, such as during pick-up and drop-off.

Hygiene and daily practices at the school and classroom level : Physical distancing of at least 1 metre between individuals including spacing of desks, frequent hand and respiratory hygiene, age-appropriate mask use, ventilation and environmental cleaning measures should be in place to limit exposure. Schools should educate staff and students on COVID-19 prevention measures, develop a schedule for daily cleaning and disinfection of the school environment, facilities and frequently touches surfaces, and ensure availability of hand hygiene facilities and national/local guidance on the use of masks.

Screening and care of sick students, teachers and other school staff : Schools should enforce the policy of “staying home if unwell”, waive the requirement for a doctor’s note, create a checklist for parents/students/staff to decide whether to go to school (taking into consideration the local situation), ensure students who have been in contact with a COVID-19 case stay home for 14 days, and consider options for screening on arrival.

Protection of individuals at high-risk: Schools should identify students and teachers at high-risk with pre-existing medical conditions to come up with strategies to keep them safe; maintain physical distancing and se of medical masks as well as frequent hand hygiene and respiratory etiquette.

Communication with parents and students : Schools should keep students and parents informed about the measures being implemented to ensure their collaboration and support.

Additional school-related measures such as immunization checks and catch-up vaccination programmes : Ensure continuity or expansion of essential services, including school feeding and mental health and psycho-social support.

Physical distancing outside classrooms : Maintain a distance of at least 1 metre for both students (all age groups) and staff, where feasible.

Physical distancing inside classrooms:

In areas with community transmission of COVID-19, maintain a distance of at least 1 metre between all individuals of all age groups, for any schools remaining open. This includes increasing desk spacing and staging recesses, breaks and lunchbreaks; limiting the mixing of classes and of age groups; considering smaller classes or alternating attendance schedules, and ensuring good ventilation in classrooms.

In areas with cluster-transmission of COVID-19, a risk-based approach should be taken when deciding whether to keep a distance of at least 1 metre between students. Staff should always keep at least 1 metre apart from each other and from students and should wear a mask in situations where 1-metre distance is not practical.

In areas with sporadic cases/no cases of COVID-19, children under the age of 12 should not be required to keep physical distance at all times. Where feasible, children aged 12 and over should keep at least 1 metre apart from each other. Staff should always keep at least 1 metre from each other and from students and should wear a mask in situations where 1-metre distance is not practical. Remote learning : Where children cannot attend classes in person, support should be given to ensure students have continued access to educational materials and technologies (internet, texting radio, radio, or television), (e.g. delivering assignments or broadcasting lessons). Shutting down educational facilities should only be considered when no alternatives are available.

Monitor your child’s health and keep them home from school if they are ill.
Teach and model good hygiene practices for your children:
Wash your hands with soap and safe water frequently. If soap and water are not readily available, use an alcohol-based hand sanitizer with at least 60% alcohol. Always wash hands with soap and water, if hands are visibly dirty.
Ensure that safe drinking water is available and toilets or latrines are clean and available at home.
Ensure waste is safely collected, stored and disposed of.
Cough and sneeze into a tissue or your elbow and avoid touching your face, eyes, mouth and nose.
Encourage your children to ask questions and express their feelings with you and their teachers. Remember that your child may have different reactions to stress; be patient and understanding.
Prevent stigma by using facts and reminding students to be considerate of one another.
Coordinate with the school to receive information and ask how you can support school safety efforts (though parent-teacher committees, etc),.
In a situation like this it is normal to feel sad, worried, confused, scared or angry. Know that you are not alone and talk to someone you trust, like your parent or teacher so that you can help keep yourself and your school safe and healthy.
Ask questions, educate yourself and get information from reliable sources.
Protect yourself and others:
Wash your hands frequently, always with soap and water for at least 20 seconds.
Remember to not touch your face, eyes, nose and mouth.
Do not share cups, eating utensils, food or drinks with others.
Be a leader in keeping yourself, your school, family and community healthy.
Share what you learn about preventing disease with your family and friends, especially with younger children
Model good practices such as sneezing or coughing into your elbow and washing your hands, especially for younger family members.
Don’t stigmatize your peers or tease anyone about being sick; remember that the virus doesn’t follow geographical boundaries, ethnicities, age or ability or gender.
Tell your parents, another family member, or a caregiver if you feel sick, and ask to stay home.

The following adaptations to transport to and from school should be implemented to limit unnecessary exposure of school or staff members.

Promote and put in place respiratory and hand hygiene, physical distancing measures and use of masks in transportation such as school buses, in accordance with local policy.
Provide tips for how to safely commute to and from school, including for public transportation.
Organize only one child per seat and ensure physical distancing of at least 1 metre between passengers in school buses, if possible. This may require more school buses per school.
If possible and safe, keep the windows of the buses, vans, and other vehicles open.

In countries or areas where there is intense community transmission of COVID-19 and in settings where physical distancing cannot be achieved, the following criteria for use of masks in schools are recommended:

1. Children aged 5 years and under should not be required to wear masks.

2. For children between six and 11 years of age, a risk-based approach should be applied to the decision to use a mask, considering:

intensity of transmission in the area where the child is and evidence on the risk of infection and transmission in this age group.
beliefs, customs and behaviours.
the child’s capacity to comply with the correct use of masks and availability of adult supervision.
potential impact of mask wearing on learning and development.
additional considerations such as sport activities or for children with disabilities or underlying diseases.

3. Children and adolescents 12 years or older should follow the national mask guidelines for adults.

4. Teacher and support staff may be required to wear masks when they cannot guarantee at least a 1-metre distance from others or there is widespread transmission in the area.

Types of mask:

Fabric masks are recommended to prevent onward transmission in the general population in public areas, particularly where distancing is not possible, and in areas of community transmission. This could include the school grounds in some situations. Masks may help to protect others, because wearers may be infected before symptoms of illness appear. The policy on wearing a mask or face covering should be in line with national or local guidelines. Where used, masks should be worn, cared for and disposed of properly.

The use of masks by children and adolescents in schools should only be considered as one part of a strategy to limit the spread of COVID-19.

Yes, ensure adequate ventilation and increase total airflow supply to occupied spaces, if possible. Clean, natural ventilation (i.e., opening windows) should be used inside buildings where possible, without re-circulating the air. If heating, ventilation and air conditioning systems are used they should be regularly inspected, maintained and cleaned. Rigorous standards for installation, maintenance and filtration are essential to make sure they are effective and safe. Consider running the systems at maximum outside airflow for two hours before and after times when the building is occupied, according to the manufacturer’s recommendations.

The following should be monitored:

effectiveness of symptoms-reporting, monitoring, rapid testing and tracing of suspected cases
the effects of policies and measures on educational objectives and learning outcomes
the effects of policies and measures on health and well-being of children, siblings, staff, parents and other family members
the trend in school dropouts after lifting the restrictions
the number of cases in children and staff in the school, and frequency of school-based outbreaks in the local administrative area and the country.
Assessment of impact of remote teaching on learning outcomes.

Based on what is learned from this monitoring, further modifications should be made to continue to provide children and staff with the safest environment possible.

Based on guidance: Considerations for school-related public health measures in the context of COVID-19

For more information please visit: Infodemic Health Topic

EPI-WIN: WHO Information Network for Epidemics

Based on guidance: Considerations for school-related public health measures in the context of COVID-19

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Published: 25 March 2023

The impact of the first wave of COVID-19 on students’ attainment, analysed by IRT modelling method

Rita Takács ORCID: orcid.org/0000-0002-0314-4179 1 ,
Szabolcs Takács ORCID: orcid.org/0000-0002-9128-9019 2 , 3 ,
Judit T. Kárász ORCID: orcid.org/0000-0002-6198-482X 4 , 5 ,
Attila Oláh 6 , 7 &
Zoltán Horváth 1

Humanities and Social Sciences Communications volume 10 , Article number: 127 ( 2023 ) Cite this article

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Universities around the world were closed for several months to slow down the spread of the COVID-19 pandemic. During this crisis, a tremendous amount of effort was made to use online education to support the teaching and learning process. The COVID-19 pandemic gave us a profound insight into how online education can radically affect students and how students adapt to new challenges. The question is how switching to online education affected dropout? This study shows the results of a research project clarifying the impact of the transition to online courses on dropouts. The data analysed are from a large public university in Europe where online education was introduced in March 2020. This study compares the academic progress of students newly enroled in 2018 and 2019 using IRT modelling. The results show that (1) this period did not contribute significantly to the increase in dropout, and we managed to retain our students.(2) Subjects became more achievable during online education, and students with less ability were also able to pass their exams. (3) Students who participated in online education reported lower average grade points than those who participated in on-campus education. Consequently, on-campus students could win better scholarships because of better grades than students who participated in online education. Analysing students’ results could help (1) resolve management issues regarding scholarship problems and (2) administrators develop programmes to increase retention in online education.

Applying the Rasch model to analyze the effectiveness of education reform in order to decrease computer science students’ dropout

Online vs in-person learning in higher education: effects on student achievement and recommendations for leadership

Medium- and long-term outcomes of early childhood education: experiences from Turkish large-scale assessments

Introduction.

During the spread of the COVID-19 pandemic, several countries closed their university buildings and switched to online education. Some opinions suggest that online education had a negative effect on dropouts because of several factors, e.g., lack of social connections, poor contact with teachers. In bachelor’s programmes—like university courses in computer science—where dropout rates were high prior to the pandemic, many questions were raised about the impact of the transition to online education.

This study focuses on the effects of the first wave of the COVID-19 pandemic on students’ dropouts and performance in Hungary. Although the manuscript addresses academic dropout, other issues such as inequality or accessibility were also covered in the research.

Theoretical background

Educational theory about student dropout in higher education.

Tinto ( 1975 ) was the first researcher who analysed the dropout phenomenon and invented the interactional theory of student persistence in higher education. He ( 2012 ) highlighted the interactions between the student and the institution regarding how well they fit in academically and socially. Interactional theories suggest that students’ personal characteristics, traits, experience, and commitment can have an effect on students’ persistence (Pascarella and Terenzini, 1983 ; Terenzini and Reason, 2005 ; Reason, 2009 ). Braxton and Hirschy ( 2004 ) also emphasized the need for community on campus as a help of social integration to develop relationships between peers because interactions with other students and faculty members crucially determine whether students persist and continue their studies or leave.

The student dropout rate has been a crucial issue in higher education in the last two decades. Attrition has serious consequences on the individual (e.g., Nagrecha et al., 2017 ) at both economic (Di Pietro, 2006 ; Belloc et al., 2011 ) and educational (Cabrera et al., 2006 ) levels. As a worldwide phenomenon, it draws the attention of policy-makers, stake-holders and academics to the necessity of seeking solutions. The dropout crisis requires complex intervention programmes for encouraging students in order to complete their studies. Addressing such a dropout crisis requires an actionable interdisciplinary movement based on partnerships among stake-holders and academics.

According to Vision 2030 studies published by the European Union, education is vital for economic development because it has a direct influence on entrepreneurship and productivity growth; at the same time, it increases employment opportunities and women empowerment. Education helps to reduce unemployment and enhance students’ abilities and skills that will be needed in the labour market. Due to students’ high attrition, the economy also suffers because experts with a degree usually contribute more to the GDP than people without (Whittle and Rampton, 2020 ).

A comparative analysis of past studies has been conducted in order to identify various causes of students’ dropout. Students’ performance after the first academic year is a topic of significant interest: the lack of students' engagement in academic life and their unpreparedness are mainly responsible for dropout after the first highly crucial period. However, further studies are necessary to better understand this phenomenon.

The characteristics of online education and its effect on dropout

Online education had already existed before the COVID-19 pandemic and had had a vast literature because online courses had been playing an important role in higher education. Online education has its own benefits, e.g., it enables students to work from the comfort of their homes with more convenient, accessible materials. In recent years, numerous investigations have been performed on how to increase the motivation of students by making them feel engaged during the learning processes (Molins-Ruano et al., 2014 ; Jovanovic et al., 2019 ). The other benefit is “humanizing”, which is an academic strategy that looks for solutions to improve equity gaps by recognizing the fact that learning situations are not the same for everyone. The aim of humanizing education is to remove the affective and cognitive barriers which appear during online learning and to provide a technique in higher education towards a more equitable future in which the success of all students is supported (Pacansky-Brock and Vincent-Layton, 2020 ). Humanizing online STEM courses has specific significance because creating such academic pathways can especially help the graduation of vulnerable, for example, non-traditional students. The definition of a non-traditional student belongs to Bean and Metzner ( 1985 ), who distinguished students by different characteristics. Non-traditional students are not on-campus students (but they can participate in online education), who are usually aged 24 years or older, and dominantly have a job and/or a family. Non-traditional students have less interaction with other participants in education, and they are much more influenced by other factors, e.g., family or other external responsibilities. Financial factors, family attitudes and external incentives can also influence dropout. The dropout model for non-traditional university students highlights that underperforming students are likely to leave the institution. Carr ( 2000 ) (in Rovai, 2003 ) noticed that persistence in online courses is regularly 10–20% lower than in on-campus courses. The dropout rate differs from institution to institution: some reports claim that 80% of students graduated, whereas other findings show that less than 50% of students completed their courses. Humanizing recognizes that engagement and accomplishment are the key factors in students’ success. Engagement and achievement are social constructs created through students’ experience. Teachers can help students to socialize and adapt to the academic environment by using humanizing practices like a liquid syllabus. Stommel ( 2013 ) also considers that hybrid pedagogy is a useful tool in order to support students’ learning because it helps teachers to implement new learning activities and facilitate collaboration among students.

Despite the various benefits that online education has, the success of students depends on the student’s capacity to independently and effectively engage in the learning process (Wang et al., 2013 ). Online learners are required to be more autonomous, as the exceptional nature of online settings relies on self-directed learning (Serdyukov and Hill, 2013 ). It is therefore especially critical that online learners, compared to their conventional classroom peers, have the self-generated capacity to control and manage their learning activities.

Online education also needs extra attention because the dropout rate is high in online university programmes. Students in online courses are more likely to drop out (Patterson and McFadden, 2009 ; in Nistor and Neubauer, 2010 ). Numerous studies reported much higher dropout rates than in the case of on-campus courses (Willging and Johnson, 2019 ; Levy, 2007 ; Morris et al., 2005 ; Patterson and McFadden, 2009 ; in Nistor and Neubauer, 2010 ). Many factors that lead to dropout were examined in the past. During online courses, students are less likely to form communities or study groups and the lack of learning support can lead to isolation. Consequently, demotivated students who were dedicated to their chosen major, in the beginning, may decide to drop out. Fortunately, there are different ways to support students who study in an online setting depending on their various psychological attributes. These psychological attributes that are connected to dropout have already been examined. One of the most noticeable hypothetical models of university persistence in online education was proposed by Rovai ( 2003 ). He claims that dropout depends on students’ characteristics e.g., learning style, socioeconomic status, studying skills, etc. Besides these factors, the method of education also has an impact on students’ decisions on whether they complete the course or drop out.

It is vital to distinguish the online education that was introduced as a consequence of the COVID-19 lockdown, when universities were forced to move their education to fully online platforms because online education had already existed in some educational institutions.

The COVID-19 pandemic and its effect on education: Inequalities in home learning and colleges’ provision of distance teaching during school closure of the COVID-19 lockdown

The lives of millions of college students were affected not only by the health and economic implications of the COVID-19 pandemic but also by the closure of educational institutions. Home and academic environments were interlaced, and most institutions were caught unprepared. In this article, we examine the effects of the transition to online learning in areas such as academic attainment.

There are several debates on the effectiveness of moving to online education. Since currently there is little literature about the COVID-19 pandemic in relation to how it affects dropouts at universities, it is worth discussing it in order to have an overview of recent studies on students’ performance. The learning environment changed radically during the first wave of the pandemic in the spring semester of 2020. The transition to home learning and teaching in such a short time without any warning or preparation raised concerns and became the focus of attention for researchers, teachers, policymakers, and all those interested in the educational welfare of students.

A potential learning loss was anticipated, possibly affecting students’ cognitive gains in the long term (Andrew et al., 2020 ; Bayrakdar and Guveli, 2020 ; Brown et al., 2020 ); in fact, an increasing number of studies suggested that the lockdown might have far-reaching academic consequences (Bol, 2020 ). In general, results suggest that students’ motivation was substantially affected by the COVID-19 pandemic and that academic and relational changes were the most notable sources of stress on both the students’ side (e.g., Rahiem, 2021 ) and the teachers’ side (e.g., Abilleira et al., 2021 ; Daumiller et al., 2021 ). Engzell et al. ( 2021 ) examined nearly 350,000 students’ academic performance before and after the first wave of the pandemic in the Netherlands. Their results suggest that students made very little development while learning from home. Closures also had a substantial effect on students’ sense of belonging and self-efficacy. Academic knowledge loss could be even more severe in countries with less advanced infrastructure or a longer period of college closures (OECD, 2020 ).

Many researchers started to examine the effects of the COVID-19 pandemic on university students’ mental health and academic performance. Clark et al. ( 2021 ) claim that university students are increasingly considered a vulnerable population, as they experience extremely high levels of stress. They draw attention to the fact that students might suffer more from learning difficulties. Daniels et al. ( 2021 ) used a single survey to collect retrospective self-report data from Canadian undergraduate students ( n = 98) about their motivation, engagement and perceptions of success and cheating before COVID-19, which shows that students’ achievements, goals, engagement and perception of success all significantly decreased, while their perception of cheating increased (Daniels et al., 2021 ). Other studies claim that during the COVID-19 pandemic, students were more engaged in studying and had higher perceptions of success. Studies also show that teachers’ strategies changed as well because of the lack of interaction between teachers and students, which led to the fact that students experienced more stress and were more likely to have difficulties in following the material presented and it could be one of the reasons for poor academic performance. Mendoza et al. ( 2021 ) investigated the relationships between anxiety and students’ performance during the first wave of the pandemic among college students. Anxiety regarding learning mathematics was measured among mathematics students studying at the Universidad Nacional de Chimborazo (UNACH) during the autumn semester of the academic year 2020. The total sample contained 120 students, who were studying the subject of mathematics at different levels. The results showed that there were statistically significant differences in the understanding of the contents presented by the teachers in a virtual way. During the COVID-19 pandemic the levels of mathematical anxiety increased. Teaching mathematics at university in an online format requires good quality digital connection and time-limited submission of assignments. This study draws attention to the negative result of the pandemic, i.e. the levels of anxiety might be greater during online education and not only in mathematics education but also in other subjects. Thus it could have an effect on students’ academic performance. However, the results are contradictory to what Said ( 2021 ) found, i.e. there was no difference in students’ performance before and during the COVID-19 pandemic. In their empirical study, they investigated the effect of the shift from face-to-face to online distance learning at one of the universities in Egypt. They compared the grades of 376 business students who participated in a face-to-face course in spring 2019 and those of 372 students who participated in the same course fully online in spring 2020 during the lockdown. A T -test was conducted to compare the grades of quizzes, coursework, and final exams of the two groups. The results suggested that there was no statistically significant difference. Another interesting result was that in some cases students had a better performance during the COVID-19 pandemic. At a large public university in Spain, Iglesias-Pradas et al. ( 2021 ) analysed the following instruction-related variables: class size, synchronous/asynchronous delivery of classes, and the use of digital supporting technologies on students’ academic performance. The research compared the academic results of the students during the COVID-19 pandemic with those of previous years. Using quantitative data from academic records across all ( n = 43) courses of a bachelor’s degree programme, the study showed an increase in students’ academic performance during the sudden shift to online education. Gonzalez et al. ( 2020 ) had similar results. Their research group analysed the effects of COVID-19 on the autonomous learning performance of students. 458 students participated in their studies. In the control group, students started their studies in 2017 and 2018, while in the experimental group, students started in 2019. The results showed that there was a significant positive effect of the COVID-19 lockdown on students’ performance: students had changed their learning strategies and improved their efficiency by studying more continuously. Yu et al. ( 2021 ) found similar results. They used administrative data from students’ grade tracking systems and found that the causal effects of online education on students’ exam performance were positive in a Chinese middle school. Taking a difference-in-differences approach, they found that receiving online education during the COVID-19 lockdown improved students’ academic results by 0.22 of a standard deviation (Yu et al., 2021 ).

Currently, there is little literature about COVID-19 in relation to how it affects students’ performance at universities, so it is worth discussing this aspect as well.

Teachers’ approach to their grading strategies and shift to online education during the COVID-19 lockdown

There is a vast literature on the limits of the capacities and challenges of online education (Davis et al., 2019 ; Dumford and Miller, 2018 ; Palvia et al., 2018 ). The lockdown during the COVID-19 pandemic created new challenges for teachers all over the world and called for innovative teaching techniques (Adedoyin and Soykan, 2020 ; Gamage et al., 2020 ; Paudel, 2020 ; Peimani and Kamalipour, 2021 ; Rapanta et al., 2020 ; Watermeyer et al., 2021 ). These changes had undoubtedly profound impacts on the academic discourse and everyday practices of teaching. Teachers’ motivations for maintaining effective online teaching during the lockdown were diverse and complex, and therefore, learning outcomes were difficult to be guaranteed. Yu et al. ( 2021 ) examined how innovative teaching could be continued during the COVID-19 pandemic, particularly by learning domain-specific knowledge and skills. The results confirmed that during the lockdown teachers who had studied online teaching methods improved their teaching skills and ICT (information and communication technology) efficacy.

Burgess and Sievertsen ( 2020 ) claim that due to the COVID-19 lockdown, educational institutions might cause major interruptions in students’ learning process. Disruption appeared not only in elaborating new knowledge but also in assessment. Given the proof of the significance of exams and tests for learning, educators had to consider postponing rather than renounce assessments. Akar and Coskun ( 2020 ) found that innovative teaching had a slight but positive relationship with creativity. From their point of view, it was not necessarily a consequence of shifting offline teaching to online platforms. Innovative teaching and digital technology were not granted and their impact on student’s performance or teachers’ grading practices is still unclear. The present research aimed to analyse students’ attainment during the COVID-19 pandemic by using student performance data. We focused on the relationship between participation in online courses and dropout decisions, which is connected to teachers’ grading. Examining how grades changed during the lockdown could give us an interesting insight into the educational inequality caused by online education regarding the scholarship system based on student’s grades.

Research questions

We know very little about the effects of transitioning to online education on student dropout and teachers’ grading practices. Even less information is available on the relationship between COVID-19 and dropout, so it is worth a discussion due to the existing controversial and interesting studies on students’ performance. This article gives a suggestion on how the scholarship system could be changed and how we could avoid inequality caused by online education. There is a scholarship system in Hungary that provides financial support to full-time programme students, based on their academic achievement.

Another issue we discuss in this article is dropping out from university programmes, which is a crucial issue worldwide. Between 2010 and 2016 at a large public university in Europe (over 30,000 students) the overall attrition rate is 30%, with the Faculty of Informatics having the worst results (60%) but nowadays these figures are more promising (30|40%). These days at least 800,000 computer scientists may be needed in Europe (Europa.eu, 2015 ), but it seems to be a worldwide issue (Borzovs et al., 2015 ; Ohland et al., 2008 ) to retain students.

This study focuses on the effects of the first wave of the COVID-19 pandemic on students’ dropout and performance in Hungary. Although the manuscript addresses academic dropout, other issues such as inequality or accessibility are also covered in the research. The aim of the paper is therefore to investigate the following questions:

It is inconclusive whether the COVID-19 pandemic had negative effects on students’ performance, which is why we claim that

Hypothesis 1: There is a significant difference in grade point averages between students who participated in online education and those in on-campus education in the second semester of their studies.

Academic achievement (in both traditional and online learning settings) can be measured by accomplishing a specific result in an online assignment and is commonly expressed in terms of a grade point average (GPA; Lounsbury et al., 2005 ; Richardson et al., 2012 ; Wang, 2010 ). According to meta-analyses, GPA is one of the best predictors of dropout (Richardson et al., 2012 ; Broadbent and Poon, 2015 ).

Hypothesis 2: In some subjects (Basic Mathematics practice, Programming, Imperative Programming lecture + practice, Functional Programming, Object-oriented Programming practice + lecture, Algorithms and Data Structures lecture + practice, Discrete Mathematics practice and Analysis practice), it was easier to obtain a passing grade in online education.

Hypothesis 3: More of the students who participated in online education dropped out than those who received on-campus education.

Difficulty and differential analysis of subjects

In the examined higher education system, a BSc programme has six semesters and every subject is graded on a five-point scale, where 1 means fail, and grades from 2 to 5 mean pass, with 5 being the best grade. In the analysis only the final grades were counted in each subject. It is important to see that in order to achieve better grades (or obtain sufficient knowledge), a subject really needs differentiation. It is worth examining the subjects of the various courses because—although there are grades—there is some kind of expected knowledge or skill that the subject should measure. Students are expected to develop these competencies or at least reach an expected level by the end of the semester. To find out whether this kind of competency actually exists (and was developed during online education) and whether the subjects measure this kind of competency, Item Response Theory (IRT) analysis was used to examine the subjects included in the computer science BSc programme. The aim of IRT analysis modelling is to bring the difficulty of the subjects and the ability of the students to the same scale (GRM, Forero and Maydeu-Olivares, 2009 ; Rasch, 1960 ). We had already successfully applied a special IRT model in order to analyse the effects of a student retention programme. In order to prevent student dropout, in a large public university in Europe, a prevention and promotion programme was added to the bachelor’s programme and an education reform was also implemented. In most education systems students have to collect 30 credits per semester by successfully completing 8|10 subjects. We conducted an analysis using data science techniques and the most difficult subjects were identified. As a result, harder subjects were removed, and more introductory courses were built into the curriculum of the first year. A further action—as an intervention—was added to a computer science degree programme: all theoretical lectures became compulsory to attend. According to the results, the dropout level decreased by 28%. The most important benefit of the education reform was that most subjects had become accomplishable (Takács et al., 2021 ). Footnote 1

Hypothesis 1 claims that the online transition due to COVID-19 during the second semester of the 2019 academic year did not result in a change in the requirement system of the subjects. Hypothesis 2 claims that essentially the same expectations were formulated by teachers. In contrast, the way teachers evaluate students necessarily changed. A subject with a given difficulty could be passed by a student with the same ability level with a given probability. Obviously, all subjects that had been less difficult were more likely to be correctly passed than more difficult subjects. The analysis was performed using the IRT, based on the STATA15 software package.

In the study, 862 students were involved in the bachelor’s computer science programme. There were 438 (415) students who started on-campus education in 2018 and 447 students who started on-campus education in 2019, but from March 2020 they participated in online education (Table 1 ). Table 1 shows the result of Hypothesis 1: The grade point average of students who participated in online education (2.5) was lower than that of students who participated in on-campus education (3.3). Table 1 also shows that 447 students participated in online education and only 19 dropped out; 438 students started on-campus education and 50 dropped out. We can conclude that there was no significant difference between students’ dropping out who participated in online education and those who received on-campus education (Hypothesis 3). Note: We can conclude that the grade point average of students who participated in online education (2.5) was lower than that of students who participated in on-campus education (3.3) (Hypothesis 1). On the other hand, there was no significant difference between the drop-out rate of students’ who participated in online education and that of those who received on-campus education (Hypothesis 3). These case numbers make it unnecessary to apply any statistical evidence because the result is obvious.

The subjects were examined by fitting a 2-parameter IRT model to them (scale 1–5 with grades, assuming an ordinal model using the STATA15 programme). ‘Grades’ mean the final grade of the subjects. The STATA15.0 software package was used for the analysis, and the Graded Response Model version of the Ordered item models was chosen from the IRT procedures (GRM; Forero and Maydeu-Olivares, 2009 ).

During the procedure, we examined two parameters: the difficulty of the items and the slope. We took into account those subjects for which the subject matter of the subject remained the same over the years, or the exams did not change substantially (exam grade, according to the same assessment criteria). However, it is important to note that obviously, not the same students completed the assignments each year.

The study involved the following subjects (only professional subjects were considered):

Mathematical Foundations

Programming

Computer Systems lecture+practice

Imperative Programming

Functional Programming

Object-oriented Programming lecture + practice

Algorithms and Data Structures I. lecture

Algorithms and Data Structures I. practice

Discrete Mathematics I. lecture

Discrete Mathematics I. practice

Analysis I. L

Analysis I. P

Examination of slope and difficulty coefficients

In this section, we examine Table 2 . As a first step, it is crucial to understand the slope indices of the given objects in different years, whether they change from one year to another. Table 2 shows the result of Hypothesis 2: In most subjects (Basic Mathematics practice, Programming, Imperative Programming lecture + practice, Functional Programming, Object-oriented Programming practice+lecture, Algorithms and Data Structures lecture + practice, Discrete Mathematics practice, and Analysis practice), it was easier to obtain a passing grade in online education.

Two parametric procedures were applied: each subject has a difficulty index and a slope.

While if the student’s ability falls short of the difficulty, the denominator of the fraction will increase, so the probability that the student will be able to pass the exam will increase—they will earn a good grade (Fig. 1 ).

Difficulty levels of the subjects in 2018 and 2019 academic year.

Instead of introducing the whole subject network, we introduce a typical subject that was analysed using the IRT. The analyses of the subject of Discrete Mathematics enable us to adequately illustrate the classic phenomenon that arose. The complete analysis of the subjects can be found in Table 2 .

The period before 2019 and after 2019 are shown separately in the table, as at the beginning of 2020 the lockdown took place when online education was introduced to all students so it had an impact on academic achievement. We presupposed that it had manifested itself in the subjects’ completing difficulty and in their ability to differentiate.

Discrete mathematics I. practice

As far as the Discrete Mathematics subject is regarded, we can observe a slope of high value above 3 (sometimes 4) before and after 2019, which means that the subject had strong differentiating abilities both before and after the COVID-19 pandemic.

There is a debate in the literature on how the performance of students changed during online education. Whereas Said ( 2021 ) found no difference in students’ performance before and during the COVID-19 pandemic, the study by Iglesias-Pradas et al. ( 2021 ) showed an increase in students’ academic performance in distance education. Gonzalez et al. ( 2020 ) predicted better results during online education than in the case of on-campus education. This study partly confirmed their result because more students tried taking the exams. However, they could not perform better as predicted by Gonzalez et al. ( 2020 ) because among computer science students those who participated in online education obtained lower grade point averages than those who participated in on-campus education. According to our results, grade point averages differed substantially between the two examined groups (Hypothesis 1). It can be seen that there are no significant differences in the study groups in terms of dropout after the first year of studies, and the number of students affected was not substantially higher/lower. There are no significant differences in dropout rates between students participating in on-campus or online education (Hypothesis 3).

The result above is crucial; however, the implications and prospective steps based on this result are even more important.

It can be seen that with the introduction of online education, more teaching and learning strategies became available for certain subjects. Teachers’ grading strategies as well as their intentions when giving grades can be assumed as the possible reasons behind the grades. These strategies on both sides (teachers’ and students’) may have appeared during online education.

There were basically two types of changes regarding the grades for the different subjects:

The difficulty associated with the particular grade of the subject in online education decreased for each value on a scale of 1–5 for a given subject (Hypothesis 2). This means that even failing (grade 1) was easier (students preferred to try the exam even if they were unprepared), or even obtaining other passing grades was easier, too. It should be noted that the examined phenomenon cannot have a negative slope (typically not 0), because a slope of 0 means that there is ½ of a probability (regardless of ability) that a student passes a given exam. Fortunately, this is not the case, so we can assume that all slopes are positive.

(a) Behind this strategy, in the case of grade 1, it can be assumed that in online education students’ general strategy was to register for the exam and try it even if unprepared in contrast to the on-campus student who would not take the exam if s/he was unprepared.

(b) It seems that it became easier to obtain a passing grade. Behind this phenomenon, strategies can be assumed from both faculty members' and students’ sides. In case of failing the exam, it makes no sense to talk about the strategy of the teacher, because the teacher was more likely to give a passing grade or even a better grade for less knowledge. In general, the thresholds for obtaining the grade were lower in all cases. This could have been illustrated by the following subjects: Basic Mathematics practice, Programming, Imperative Programming lecture + practice, Functional Programming, Object-oriented Programming practice + lecture, Algorithms and Data Structures lecture + practice, Discrete Mathematics practice and Analysis practice.

Analysing further the subjects by IRT modelling, we saw that it was easier to obtain lower grades (grades 1, 2 and 3). However, in the case grade 4 or 5, it appears that it was more difficult to obtain them due to the prevalence of the higher requirements of the subjects.

(a) The insufficient grades’ (i.e. grade 1) lower level of difficulty (shown by the IRT model) clearly showed that there was no substantial difference in this respect compared to obtaining insufficient grades during the on-campus or online education period.

(b) The results showed that obtaining good grades (4 or 5) became more difficult during online education. It can be assumed that students participating in online education require some kind of help from education management in order to compensate for the disadvantages posed by distance learning because they got worse grades and worse average grade points as compared to on-campus students.

In the following, we examine what strategies faculty members and students may apply considering the difficulty of each grade of the subjects (left column of Table 2 ) showed a decreasing trend.

From the students’ point of view, isolation could result in students being involved in studying more effectively. Consequently, the time spent on the elaboration of the subjects may increase (Wang et al., 2013 ) compared to in-class education and by using available materials, textbooks, practice assignments, students could devote extra energy to subjects, which may result in better exam grades.

From the teachers’ point of view, teachers might want to offer some ‘compensation’ at exams due to non-traditional teaching. In light of this, they are likely to ask a ‘slightly easier’ question, adapt them to the practice tasks, or even lower the exam requirements, e.g., lowering the score limits by 1-2 points more favourable, or accepting answers that would not be accepted in other circumstances.

Note that these two strategies may have been present at the same time: the teacher perceived increased student contribution during the semester, for example, greater activity in online classes, and therefore, provided them with some reward by giving better final grades after taking into consideration their overall performance during the semester.

Please note that both narratives could appear at the same time.

It is also important to see that although grade point averages shifted, the shift was not necessarily drastic, and dropout rates did not improve. It may also be legitimate that there were individual characteristics that caused the difference in the grade point average.

From the student’s point of view, it could also mean that they were prepared in the same way in online education as in in-class education for exams. However, the same strategy did not necessarily result in better grades in the upper segment (obtaining 4 or 5).

The teacher determined the minimum level of requirements, either for mid-term achievements or final assignments and communicated it clearly to the students. How to obtain a passing grade was clear to the students. However, how to obtain good and excellent grades would have required more serious preparation and self-directed learning in online settings.

It is important to see that subjects, where it was more difficult to obtain better grades, were mainly theoretical ones (e.g.: lectures). They were tested mostly by oral exams where it was not possible to use additional materials, they had to answer directly to the questions. In this respect, teachers’ explanations, for example, could lead to very serious shortcomings in the case of knowledge transfer as well as the transfer of the same levels of the previous examination systems. This could result in lower achievement in areas where teachers’ explanations would have been necessary. Students had a harder time bridging the online-offline gap.

Education management issues

In the higher education system analysed, students receive a scholarship according to their grade point average achievement. It is calculated based on the average of the final grades received at the end of the semester and the credits earned. It is worth considering that for online systems, credit-weighted averages will not necessarily show students’ real knowledge. This also results in serious problems when it comes to rewarding students’ performance with a scholarship, where multiple types of educational models may conflict.

This is because whether students can successfully complete a subject differs greatly in an online education system but subjects seem to have become fundamentally easier.

Thus, different education systems (in-class education and online education) can lead to different grading results, so it is not advisable to apply the same scholarship system because it can be fundamentally unfair (some fields can become easier or more difficult).

The results of this study imply that COVID-19 had various effects on the education sector. The results are discussed in connection with the introduction of online education during the COVID-19 pandemic in terms of dropouts. The teachers who were involved in this study were the same during online education and on-campus education. This is the reason why we can conclude that the results also seem to suggest that teachers tried to compensate for the negative effects of the pandemic by bringing in pedagogical strategies aimed at ensuring that students could more easily obtain passing grades in examinations. Similarly, according to Mendoza et al. ( 2021 ), the failures of online education had a direct impact on student’s performance and learning.

This study found that students achieved better results during in-class education, which offers interesting implications for teaching practice. The results suggest that organizational support and flexible structures are needed in order to adapt teaching to the new circumstances set by the crisis. Higher education institutions should pay careful attention to developing students’ skills as well as to seeking ways to quickly respond to environmental changes while sustaining the delivery of high-quality education.

In the literature review, contradictory results were found for students’ performance during online education; therefore, this result contends previous literature and should be further explored.

A substantial difference in grade point averages can be found between the two examined groups. The first hypothesis was confirmed: students who participated in on-campus education obtained better grade point averages than students of online education. The teachers declared the minimum level of requirement and communicated it to the students quite clearly. It is a thought-provoking result that for online education, credit-weighted grade point averages would not necessarily show real knowledge well.

The second hypothesis was also proved because some subjects became easier to pass in online education, at least obtaining a passing grade. Online education facilitated students’ strategies e.g., creating an agenda of studying was essential to maintain effective and continuous learning.

The third hypothesis was not confirmed because significant differences in dropout rates were not found between the students who participated in online education and on-campus education. The dropout rate remained nearly unchanged between students who participated in online education (19 students dropped out), and students who participated in on-campus education (50 students dropped out). Introducing online education was effective or at least not harmful in terms of dropout because the dropout rate remained unchanged, compared to the previous year.

The results suggest that regarding dropout rates, there was no significant difference between online and on-campus education. The result suggests several assumptions: e.g.: the teachers had been more indulgent, as they also found it more difficult to communicate effectively during the COVID-19 period and were less able to apply with traditional methods. The process of knowledge transfer moved to online platforms and a different kind of interaction could be applied to rely on the online education system.

Limitations of the study and future research

This study proposed research clarifying the impact of the transition to online courses on dropout. The results show that this period did not contribute significantly to the increase in dropouts. Subjects became more achievable during online education. Students who participated in online education reported lower average grade points than students who participated in on-campus education. Consequently, on-campus students could win better scholarships than students who participated in online education because of better grades.

Several other factors e.g., whether students have met in person in the past, could affect the dropout and grade point averages which were not taken into consideration in this research. In the future, it is recommended to measure students’ current level of knowledge, how much they can adapt to online education, and how they would react in the next similar crisis.

Even though this study presents interesting results, the authors believe that the conclusions derived from them should be interpreted carefully. It allows both researchers and teachers to develop further methods to examine students’ strategies in online education during the COVID-19 period. Future research should be extended with additional variables. Data analysis techniques should also be taken into consideration in order to evaluate the academic profile of students who dropped out in previous years. Limitations include that analysis does not entirely reflect the true engagement of students in the education system because only the first two semesters were examined.

The results of this study open new lines of similar research. It is hoped that other researchers will consider examining the potential impact of COVID-19 on educational planning and scholarship systems. The results of this study can further be validated by considering a wider study that would collect both quantitative and qualitative data to give a deeper understanding of the effects of this epidemic.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

For a detailed explanation of the method see Takács et al. ( 2021 ).

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Acknowledgements

The described article was carried out as part of the EFOP 3.4.3-16-2016-00011 project in the framework of the Széchenyi 2020 programme. The realization of these projects is supported by the European Union, co-financed by the European Social Fund.

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TR contributed to the design of the study and data interpretation. As principal author, she coordinated the writing process of the manuscript. KJ and TS are researchers that study the dropout phenomenon across higher education, and therefore have participated on each phase of this research. OA and HZ have largely contributed to the analysis and interpretation of data, and consequently to the understanding of the phenomenon. Every author have played a remarkable role in the writing of this article.

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Takács, R., Takács, S., Kárász, J.T. et al. The impact of the first wave of COVID-19 on students’ attainment, analysed by IRT modelling method. Humanit Soc Sci Commun 10 , 127 (2023). https://doi.org/10.1057/s41599-023-01613-1

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Covid-19 infection and...

Covid-19 infection and vaccination during first trimester and risk of congenital anomalies: Nordic registry based study

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Peer review
Jonas Söderling , statistician 2 ,
Anne K Örtqvist , associate professor 2 3 ,
Anne-Marie Nybo Andersen , professor 4 ,
Olof Stephansson , professor 2 5 ,
Siri E Håberg , director 1 6 ,
Stine Kjaer Urhoj , associate professor 4 7
1 Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
2 Clinical Epidemiology Division, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
3 Department of Obstetrics and Gynaecology, Visby County Hospital, Visby, Sweden
4 Department of Public Health, University of Copenhagen, Copenhagen, Denmark
5 Department of Women's Health, Karolinska University Hospital, Solna, Stockholm, Sweden
6 Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
7 Statistics Denmark, Copenhagen, Denmark
Correspondence to: MC Magnus maria.christine.magnus{at}fhi.no
Accepted 6 May 2024

Objectives To evaluate the risk of major congenital anomalies according to infection with or vaccination against covid-19 during the first trimester of pregnancy.

Design Prospective Nordic registry based study.

Setting Sweden, Denmark, and Norway.

Participants 343 066 liveborn singleton infants in Sweden, Denmark, and Norway, with an estimated start of pregnancy between 1 March 2020 and 14 February 2022, identified using national health registries.

Main outcome measure Major congenital anomalies were categorised using EUROCAT (European Surveillance of Congenital Anomalies) definitions. The risk after covid-19 infection or vaccination during the first trimester was assessed by logistic regression, adjusting for maternal age, parity, education, income, country of origin, smoking, body mass index, chronic conditions, and estimated date of start of pregnancy.

Results 17 704 (5.2%) infants had a major congenital anomaly. When evaluating risk associated with covid-19 infection during the first trimester, the adjusted odds ratio ranged from 0.84 (95% confidence interval 0.51 to 1.40) for eye anomalies to 1.12 (0.68 to 1.84) for oro-facial clefts. Similarly, the risk associated with covid-19 vaccination during the first trimester ranged from 0.84 (0.31 to 2.31) for nervous system anomalies to 1.69 (0.76 to 3.78) for abdominal wall defects. Estimates for 10 of 11 subgroups of anomalies were less than 1.04, indicating no notable increased risk.

Conclusions Covid-19 infection and vaccination during the first trimester of pregnancy were not associated with risk of congenital anomalies.

Introduction

Women infected with covid-19 during pregnancy have a higher risk of pregnancy complications. 1 2 3 Based on this evidence, and studies showing that pregnant women are at increased risk of severe disease from covid-19, 3 4 the authorities in most countries recommend that pregnant women get vaccinated against covid-19. 5 6 7 8 Because pregnant women are not often included in randomised controlled trials of vaccines before marketing, evidence relating to the safety of vaccines during pregnancy relies on observational data after the introduction of vaccines. Vaccination of pregnant women against covid-19 was therefore recommended before conclusive safety data were available. Studies evaluating the safety of covid-19 vaccines among pregnant women after marketing have been reassuring, providing no evidence of an increased risk of pregnancy complications. 9 10 11 12

Limited evidence is available about the risk of major congenital anomalies after infection with 13 14 or vaccination against 15 16 17 18 19 covid-19. A study of 92 pregnancies in women infected with covid-19 during the first trimester and 292 without covid-19 infection (data from the International Registry of Coronavirus Exposure in Pregnancy) indicated no increased risk of any major congenital anomalies (relative risk 1.2, 95% confidence interval (CI) 0.3 to 4.2). 13 A registry based study from Scotland reported similar findings of no increased risk of congenital anomalies after infection with covid-19 (1574 with infection and 4722 without infection; adjusted odds ratio 0.94; 95% CI 0.57 to 1.54), in addition to no increased risk with covid-19 vaccination (6731 with vaccination and 20 193 without vaccination; adjusted odds ratio 1.00, 95% CI 0.81 to 1.22). 18 An Israeli study of 24 288 pregnancies (2134 in women vaccinated against covid-19 during the first trimester) also reported no increased risk of congenital anomalies after vaccination (relative risk 0.69, 95% CI 0.44 to 1.04). 16 A smaller study in the United States found that 27 of 534 infants whose mothers were unvaccinated against covid-19 and 109 of 2622 infants whose mothers were vaccinated any time during pregnancy were diagnosed with congenital anomalies (P value 0.35). 17 Finally, a study of 1450 pregnancies in the COVI-PREG registry from Switzerland and France did not find evidence of an increased risk of congenital anomalies after vaccination against covid-19 during the first trimester (adjusted relative risk 0.89, 95% CI 0.12 to 6.80). 19

Most of these studies had an inadequate sample size to robustly examine these rare outcomes, 13 15 17 19 were not able to study first trimester exposure, 15 18 or did not investigate subgroups of congenital anomalies. 15 16 17 19 For congenital anomalies, first trimester exposure is of particular interest. 20 Because not all major congenital anomalies are detected at birth, follow-up information from the first year of life is important to reduce misclassification. 21 As a consequence, it has only recently become possible to study these outcomes after covid-19 infection and vaccination in early pregnancy. The objective of this study was to study the risk of major congenital anomalies according to infection with or vaccination against covid-19 during the first trimester.

Study population

We studied liveborn singleton infants in Sweden, Denmark, and Norway with estimated start of pregnancy between 1 March 2020 and 14 February 2022. Births were identified through the Swedish Pregnancy Register, 22 the Danish National Patient Register (registrations of international classification of disease, 10th revision (ICD-10) codes Z38, O80-84, and P95), 23 and the Medical Birth Registry of Norway. 24 The Danish and Norwegian data included all births nationally, while the Swedish data included 94% of all births (in 18 of 21 Swedish regions). We required a minimum of nine months (275 days) of postnatal follow-up (end of follow-up in the three different national registry linkages was 31 March 2023 for Sweden, 31 December 2022 for Denmark, and 15 September 2023 for Norway). To avoid oversampling of preterm pregnancies, we excluded births that were not able to reach 42 completed gestational weeks and have nine months of postnatal follow-up by the end of follow-up in the national linkages. We obtained information on maternal socioeconomic measures, infections with covid-19, and vaccination against covid-19 from national databases (see supplementary appendix for details).

Covid-19 infection and vaccination

The exposures of interest were infection with or vaccination against covid-19 during the first trimester (13 weeks plus six days). The two exposures were evaluated separately. We did not evaluate the role of a combined exposure, or exclude those exposed to the other exposure of interest from the reference group. The start of pregnancy was estimated from the date of birth minus the gestational age in days (the gestational age of the pregnancy was estimated by ultrasound for more than 90% of births, or by day of last menstrual period). Information on laboratory confirmed polymerase chain reaction (PCR) positive tests for covid-19 was obtained from mandatory reports to SmiNet at the Public Health Agency for Sweden, 25 from the Norwegian Surveillance System for Communicable Diseases for Norway, 26 and information on PCR and antigen positive tests was obtained from the Microbiology Database at the State Serum Institute for Denmark. 27 28 In Denmark, positive antigen tests that were followed by a negative PCR test within four days were excluded, and 15% of the included positive tests were only based on antigen tests. Until around March to April 2022, pregnant women who tested positive on a self-administered antigen test were advised to obtain a confirmatory PCR test.

Information on vaccination was obtained from mandatory national vaccination registries. We restricted the analysis to the two mRNA vaccines from Pfizer-BioNTech (BNT162b2) and Moderna (mRNA-1273), and excluded women who had received other covid-19 vaccines. At the beginning of the covid-19 pandemic, vaccination during the first trimester was not recommended for the general population of pregnant women, but could be considered for those at high risk. Pregnant women were advised to get vaccinated from the second trimester onwards, starting from May 2021 in Sweden, July 2021 in Denmark, and August 2021 in Norway. Women vaccinated during the first trimester at the beginning of the pandemic include those who were vaccinated before realising they were pregnant, and those who had a particularly high risk of infection or severe disease from covid-19 owing to underlying chronic conditions or their job (ie, healthcare workers). General recommendations for pregnant women to get vaccinated during the first trimester started in January 2022 in Norway, but these recommendations were not issued in Sweden and Denmark. eTable 1 gives a brief overview of major changes to recommendations.

Congenital anomalies

We defined major congenital anomalies identified during the first nine months of life according to the EUROCAT (European Surveillance of Congenital Anomalies) classification, guide 1.5. 29 Information on congenital anomalies was based on data from the National Birth Registry (Norway), the Pregnancy Register (Sweden), and national patient registries (all countries). The national patient registries include all inpatient and outpatient contact with specialist healthcare services based on mandatory reporting. Four digit ICD-10 codes (QXX.XX) were not available in Norway and Sweden, and so three digit codes were used (QXX.X). Anomalies were categorised as any major congenital anomaly, congenital heart defects, nervous system anomalies, eye anomalies, ear, face and neck anomalies, respiratory anomalies, oro-facial clefts, gastrointestinal anomalies, abdominal wall defects, congenital anomalies of kidney and urinary tract, genital anomalies, and limb anomalies. eTable 2 presents the ICD-10 codes used to define the subgroups of anomalies. We do not show results when less than five infants with maternal exposure were reported across the three countries.

We obtained information on maternal age (<25, 25-29, 30-34, 35-39, and ≥40 years), parity (0, 1, ≥2), maternal educational level (≤9 years, 10-12 years, >12 years), household income based on the national distributions (in thirds), maternal region of birth (Scandinavia, other European countries, Middle East or Africa, other or unknown), estimated date of start of pregnancy (estimated as date of birth minus gestational age in days; continuous), smoking in pregnancy (yes or no), body mass index before pregnancy or early in pregnancy (World Health Organization categories: underweight, normal weight, overweight, obese, unknown), and pre-existing chronic condition before pregnancy (yes or no; included hypertension, chronic kidney disease, asthma, cardiovascular disease, thrombosis, diabetes, and epilepsy). eTable 3 presents information on the ICD-10 codes used to define these chronic conditions. All of these covariates were considered potential confounders for infection with and vaccination against covid-19.

Statistical analysis

All analyses were conducted separately for each country, and subsequently combined using a random effects meta-analysis. We developed a detailed analysis plan harmonising the definition of all variables before starting the analysis, which was followed by the analysts in all three countries. Common analysis scripts were developed for Denmark and Norway using Stata, while separate scripts were developed for Sweden using SAS. Heterogeneity between the countries was examined using the I 2 statistic. We do not show country specific estimates for legal reasons relating to small numbers of certain outcomes. We first examined the risk of congenital anomalies after infection with covid-19 during the first trimester using logistic regression. The multivariable model adjusted for maternal age at the start of pregnancy, parity, educational level, household income level, maternal region of birth, estimated date of start of pregnancy, smoking during pregnancy, body mass index before or early in pregnancy, any history of chronic conditions, and vaccination against covid-19 during the first trimester. We accounted for the dependency between multiple pregnancies to the same woman by using cluster variance estimation. We also explored differences in the risk of congenital anomalies according to the covid-19 viral variants using the same calendar time cut-off points for major circulating variants, as previously published. 30

We also evaluated the risk of congenital anomalies according to vaccination against covid-19 during the first trimester. This analysis was restricted to pregnancies starting after 1 January 2021 when vaccines became available. The reference group comprised women not vaccinated in the first trimester (including women vaccinated before pregnancy and after the first trimester). The multivariable model was adjusted for the same covariates as in analyses of covid-19 infection, in addition to infection with covid-19 during the first trimester. We also evaluated differences according to the two different mRNA vaccines (BNT162b2 and mRNA-1273), and conducted a sensitivity analysis that excluded infants of mothers who remained unvaccinated at the end of follow-up (21%).

For both exposures, we conducted two sensitivity analyses. In the first sensitivity analysis, we required at least 12 months of follow-up after birth. 21 In the second sensitivity analysis, we excluded infants with congenital anomalies that are known to have a main genetic cause (defined by the following codes: Q619, Q751, Q754, Q771, Q772, Q780, Q796, Q821, Q85, Q87, Q90-99). 31 The analyses were conducted using Stata version 17 (Statacorp, TX, USA) and SAS version 9.4 (SAS Institute, NC, USA).

Patient and public involvement

Because this study was based on deidentified data from national health registries, it was not permitted or possible to contact any registered individuals directly. The advisory group responsible for national guidelines for vaccination of pregnant women against covid-19 at the Norwegian Institute of Public Health provided important feedback and was informed of preliminary findings throughout the project. The researchers did not have the necessary infrastructure or funding available to further pursue additional patient or public partnership for this specific project.

A total of 343 066 singleton infants were included in the analysis of covid-19 infection ( fig 1 ). The distribution of background characteristics was very similar across the three countries ( table 1 ). There was a slightly higher proportion of infants born to women from the Middle East or Africa in Sweden, while the proportion of mothers with an underlying chronic disease appeared to be lower in Denmark than in Sweden and Norway. A total of 17 704 infants were diagnosed with a major congenital anomaly within a nine month follow-up, corresponding to 516 per 10 000 live births. Only 737 out of 17 704 (4.2%) had anomalies in two or more major congenital anomaly subgroups. Table 2 shows the rates of major congenital anomalies. Figure 2 shows the gestational age distributions for date of covid-19 infection (date of registered positive test) and vaccination against covid-19 during the first trimester, while eFigure 1 shows the distribution of calendar time for the start of pregnancy, infection with and vaccination against covid-19 during the first trimester.

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Maternal background characteristics among infants in each country

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Rates of EUROCAT (European Surveillance of Congenital Anomalies) categories of major congenital anomalies

Gestational age distribution of infection with and vaccination against covid-19 during first trimester

Risk of congenital anomalies according to infection with covid-19

A total of 10 229 infants (3%) had mothers with covid-19 infection during the first trimester. These mothers had higher parity, lower educational level, lower household income level, and were more likely to be born in the Middle East or Africa (eTable 4). We did not find an increased risk of any major congenital anomalies after infection with covid-19 during the first trimester, with an adjusted odds ratio of 0.96 (95% confidence interval 0.87 to 1.05; table 3 ). Likewise, we did not find an increased risk of specific subgroups of congenital anomalies after maternal infection, with adjusted odds ratio ranging from 0.84 (0.51 to 1.40) for eye anomalies to 1.12 (0.68 to 1.84) for oro-facial clefts ( table 3 ). There was some heterogeneity in the risk estimates for different congenital anomalies according to exposure to infection during the first trimester (I 2 statistic: 11% for kidney and urinary anomalies, 29% for gastrointestinal anomalies, 50% for limb anomalies, and 0% for the other outcomes evaluated). Exposure to the index variant of covid-19 during the first trimester occurred in 3124 pregnancies, with 2065 to alpha and 5040 to delta variants. We observed no notable differences in the risk according to these three viral variants, although the results were associated with a high degree of uncertainty (eTable 5). Analyses among infants with at least 12 months of follow-up (compared with at least nine months in the main analysis), or excluding infants with presumed genetically related anomalies showed similar results (eTables 6 and 7).

Risk of congenital anomalies according to infection with covid-19 during the first trimester

Risk of congenital anomalies according to vaccination against covid-19

We included 152 261 infants in the analysis of congenital anomalies according to vaccination ( fig 1 ). Among these, 29 135 (19%) had maternal exposure to covid-19 vaccination during the first trimester: 22 322 (77%) mothers received the BNT162b2 vaccine and 6813 (23%) had the mRNA-1273 vaccine. Mothers vaccinated against covid-19 during the first trimester had higher education and household income, were more likely to have an underlying chronic disease, and were more likely to be overweight or obese (eTable 8).

We did not find an increased risk of any major congenital anomaly among infants whose mothers were vaccinated against covid-19 during the first trimester, with an adjusted odds ratio of 1.03 (95% CI 0.97 to 1.09; table 4 ). When we examined subgroups of anomalies, adjusted estimates ranged from 0.84 (0.31 to 2.31) for nervous system anomalies to 1.69 (0.76 to 3.78) for abdominal wall defects ( table 4 ). The estimates for 10 of 11 of the subgroups of anomalies were less than 1.04, indicating no notable increased risk. There was some heterogeneity in the estimates for the risk of the different congenital anomalies according to exposure to vaccination during the first trimester (I 2 statistic: 32% for congenital heart defects, 40% for gastrointestinal anomalies, 44% for kidney and urinary anomalies, 76% for nervous system anomalies, and 0% for the other outcomes evaluated).

Risk of congenital anomalies according to vaccination against covid-19 during the first trimester

Restricting our analysis to infants with at least 12 months of follow-up, excluding infants with genetic disorders, or excluding infants of mothers who remained unvaccinated at the end of follow-up yielded similar results (eTables 9-11). We found no clear evidence of a difference in the risk of congenital anomalies according to exposure to the two different mRNA vaccines (eTable 12). Only 386 infants were exposed to infection and vaccination during the first trimester, therefore we did not evaluate the role of a combined exposure, or exclude those exposed to the other exposure of interest from the reference group.

Including fetal deaths and late induced abortions

In Norway, we had information on all fetal deaths and induced abortions after 12 completed gestational weeks in the birth registry. These data included 1227 pregnancies during the follow-up period in addition to the live births being studied. Of these 1227 pregnancies ending in a fetal death or induced abortion, 64 (rate 522 per 10 000 pregnancies) had a major congenital anomaly registered. When we examined the risk of any major congenital anomaly according to infection including these pregnancies, the results were similar to the main results (adjusted odds ratio 0.98, 95% CI 0.86 to 1.11 including these additional pregnancies v 0.97, 0.85 to 1.11 in the main analysis) and with vaccination against covid-19 (1.07, 0.97 to 1.17 v 1.06, 0.97 to 1.17). No information on terminations with data on anomalies were available for Denmark and Sweden.

Principal findings

Our Nordic registry based study did not find an increased risk of any major congenital anomalies among infants whose mothers had covid-19 infection or covid-19 vaccination during the first trimester. No notable heterogeneity in the risk was apparent according to viral variants, although larger studies are needed to provide more robust evidence.

Strengths and limitations

Strengths of the current study include the large population based sample, the inclusion of data from several countries, and the evaluation of subgroups of anomalies. The rate of major congenital anomalies in our study is higher than that reported in the EUROCAT statistics for the included countries. This difference is probably explained by the lack of follow-up after birth for most regions in the EUROCAT statistics, changes in the definitions according to the latest updated version of the EUROCAT guidelines, and misclassification resulting in false positive registrations 32 —for example, when a child is under evaluation or being diagnosed. We do not expect any of these points to differ according to the exposures of interest, and any such non-differential misclassification could therefore have resulted in an underestimation of the associations of interest. As an example, we examined the limb anomaly subgroup further because the Danish rate for limb anomalies was especially high—primarily because of inclusion of hip anomalies recorded between birth and six weeks of age, which have been shown to have a high false positive rate. 33 When excluding these diagnoses, the rate more than halved, but the overall estimates for the risk of limb anomalies according to covid-19 infection or vaccination were not appreciably affected.

The study also has limitations. Our analysis is restricted to live births. We chose not to include stillbirths in the main analysis because the presence of congenital anomalies is poorly recorded for this group, and we did not have information on fetal deaths or induced abortions for all countries. It is unlikely that our analyses of vaccination against covid-19 and risk of congenital anomalies is biased owing to exclusion of fetal deaths because no increased risk of miscarriage or stillbirth according to vaccination was observed. 11 34 However, the results for infection with covid-19 might be underestimated because an increased risk of stillbirth with covid-19 infection has been reported previously in our study population, and we do not know if congenital anomalies were contributing factors in these stillbirths. 30 Differences in the testing strategy could have influenced our ability to identify women infected with covid-19.

We also acknowledge that we did not capture self-administered antigen tests for covid-19. However, for most of the study period, women who tested positive using an antigen test were advised to get a confirmatory PCR test. In Denmark, from March 2021 until March 2022, 80-90% of positive antigen tests were followed up by a confirmatory PCR test. 35 Similar estimates are unfortunately not available for Sweden and Norway. It is possible that women with a higher risk of having a baby with a congenital anomaly had a greater likelihood of getting tested, for example older women and those with various underlying chronic conditions and using drugs, or women with a previous child with anomalies. It is also possible that pregnancies followed for congenital anomalies in specialised antenatal care or fetal medicine units were more likely to be tested for covid-19. We only had three digit ICD-10 codes available in Norway and Sweden, and we chose to be conservative in the exclusion of minor anomalies, which might have resulted in an underestimation of some of the associations of interest.

There might also be unmeasured or residual confounding influencing our results, for example through unmeasured factors such as differences in underlying genetic risk, use of drugs, or from measurement error or categorisation of included confounders. However, because none of the included factors are very strong risk factors for congenital anomalies, we do not expect the categorisation to be a large problem. The adjustment for underlying chronic conditions should account partially for use of drugs for these conditions. Moreover, we believe it is the underlying conditions, and not the drugs themselves, which might affect the likelihood of covid-19 infection and vaccination. To explore the scope of drug use, we checked how many pregnant women in Sweden and Norway were taking teratogens from a predefined list, 36 and found that only 104 pregnancies across the two countries had maternal exposure during the first trimester. Because the use of these drugs was so rare, it is unlikely that it influenced our results. The role of underlying genetic risk might be further evaluated in larger datasets using a sibling comparison. Unmeasured confounding could have led us to overestimate the associations of interest. However, because our findings are largely null, if associations were even weaker then our conclusion of no indication of adverse effects of vaccinations still holds.

Comparison with other studies

Our findings are in line with previous studies indicating no strong evidence for an increased risk of any major congenital anomalies after infection with 13 14 or vaccination against 15 16 17 18 19 covid-19. Most of these existing studies had a modest sample size and inadequate power, 13 15 17 19 and were therefore not able to examine subgroups of congenital anomalies. 15 16 17 19 The studies that examined subgroups of anomalies indicated no notable increased risk after infection or vaccination. 14 18 These studies also had limited postnatal follow-up, and are likely to have underestimated the number of anomalies. Therefore, we add to the current evidence with our results showing that there appears to be no robust evidence of an increased risk of any of the subgroups of congenital anomalies as defined by the EUROCAT classification. Additionally, we provide some exploratory evidence that there do not appear to be any major differences in the risk according to covid-19 viral variants, although these results should be further explored in future studies.

Policy implications

Evidence supports an increased risk of certain pregnancy complications, including preterm birth and stillbirth, among women with covid-19 infection during pregnancy. 3 30 We did not find any evidence of an increased risk of congenital anomalies after covid-19 infection, but newer variants were not included. However, current knowledge is in line with the new viral variants becoming less harmful. 37 Vaccination of pregnant women protects the women and the infants from adverse outcomes. Furthermore, we did not find any indication that vaccination against covid-19 during the first trimester increased the risk of anomalies, providing additional evidence about the safety of vaccination in pregnant women. Overall, our findings support the current recommendations to vaccinate pregnant women against covid-19.

Conclusions

Covid-19 infection and vaccination during the first trimester of pregnancy were not associated with risk of congenital anomalies.

What is already known on this topic

Existing studies on the risk of major congenital anomalies after infection with or vaccination against covid-19 are limited

Because the first trimester is the most important time for covid-19 exposure, and postnatal follow-up is necessary to identify anomalies not observed at birth, studies of any increased risk of major congenital anomalies have only recently become possible

What this study adds

Covid-19 infection or vaccination during the first trimester was not associated with congenital anomalies

Any differences in the risk of congenital anomalies according to viral variants of covid-19 should be further examined in future studies

Ethics statements

Ethical approval.

This study was approved by the Regional Committee for Medical and Health Research Ethics of South/East Norway (No 141135), and the Swedish Ethical Review Authority (No 2020-01499, 2020-02468, 2021-00274). Each committee provided a waiver of consent for participants. In Denmark, ethical approval is not required for registry studies, however the study was registered with the Danish Data Protection Agency via Statistics Denmark.

Data availability statement

The data used in this study are available through application to Pregnancy Register (Datauttag | Graviditetsregistret, medscinet.com ), National Board of Health and Welfare ( https://bestalladata.socialstyrelsen.se/data-for-forskning/ ) and Statistics Sweden ( https://www.scb.se/vara-tjanster/bestall-data-och-statistik/ ) in Sweden; Statistics Denmark ( https://www.dst.dk/en/TilSalg/Forskningsservice/Dataadgang ), the Danish Health Data Authorities ( https://sundhedsdatastyrelsen.dk/da/forskerservice/om-forskerservice/nyheder_forskerservice/vaccinedata_031023 ), and Statens Serum Institute ( https://miba.ssi.dk/forskningsbetjening ) in Denmark; and the Norwegian Institute of Public Health in Norway ( helsedata.no/en/ ). The analysis scripts are available upon request to the corresponding author.

Contributors: MCM, JS, and SKU conceived and designed the study. OS, AMNA, and MCM obtained access to data. MCM, JS, and SKU conducted the data analysis. MCM drafted the initial version of the manuscript. AKO and SEH provided important insight during the data analysis. All authors contributed in the interpretation of the data and critically revised the manuscript. All authors had full access to the data in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. MCM is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: Supported by the Research Council of Norway (project No 324312) through its Centres of Excellence funding scheme (project No 262700) and by NordForsk (project No 105545 and 135876). MCM received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant No 947684). The funders had no role in the completion of the research project, the writing of the manuscript for publication, or the decision to publish the results.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from Research Council of Norway, NordForsk, European Research Council for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Transparency: The lead author (the manuscript’s guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: Dissemination to the general population will be made through media outreach, including a press release and communication through social media outlets by the host institution, on publication of this study.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

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COVID-19 in Primary and Secondary School Settings During the First Semester of School Reopening — Florida, August–December 2020

Weekly / March 26, 2021 / 70(12);437–441

On March 19, 2021, this report was posted online as an MMWR Early Release.

Timothy Doyle, PhD 1 ,2 ; Katherine Kendrick, MPH 1 ; Thomas Troelstrup, MPH 1 ; Megan Gumke, MPH 1 ; Jerri Edwards 3 ; Shay Chapman, MBA 3 ; Randy Propper, PhD 1 ; Scott A. Rivkees, MD 4 ; Carina Blackmore, DVM, PhD 1 ( View author affiliations )

What is already known about this topic?

Limited U.S. data have been reported regarding COVID-19 in students and school staff members as kindergarten through grade 12 (K–12) schools have reopened.

What is added by this report?

COVID-19 school-related disease incidence among Florida students was correlated with community incidence in the counties observed and was highest in smaller counties, districts without mask requirements, and those that reopened earliest after closure in March 2020. Incidence increased with the proportion of students receiving in-person instruction. Fewer than 1% of registered students were identified as having school-related COVID-19.

What are the implications for public health practice?

Both community-level and school-based mitigation measures are important in limiting transmission of COVID-19; school reopening can likely be achieved without widespread student illness in K–12 settings.

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After detection of cases of COVID-19 in Florida in March 2020, the governor declared a state of emergency on March 9,* and all school districts in the state suspended in-person instruction by March 20. Most kindergarten through grade 12 (K–12) public and private schools in Florida reopened for in-person learning during August 2020, with varying options for remote learning offered by school districts. During August 10–December 21, 2020, a total of 63,654 COVID-19 cases were reported in school-aged children; an estimated 60% of these cases were not school-related. Fewer than 1% of registered students were identified as having school-related COVID-19 and <11% of K-12 schools reported outbreaks. District incidences among students correlated with the background disease incidence in the county; resumption of in-person education was not associated with a proportionate increase in COVID-19 among school-aged children. Higher rates among students were observed in smaller districts, districts without mandatory mask-use policies, and districts with a lower proportion of students participating in remote learning. These findings highlight the importance of implementing both community-level and school-based strategies to reduce the spread of COVID-19 and suggest that school reopening can be achieved without resulting in widespread illness among students in K–12 school settings.

Florida has one independent school district in each of its 67 counties. For the 2020–21 school year, 2,809,553 registered students were enrolled in approximately 6,800 public, charter, and private K–12 schools, ranging from 707 to 334,756 students per school district. In response to the COVID-19 pandemic, some school districts delayed the start of the 2020–21 academic year after suspension of in-person learning in March. Most schools resumed in-person instruction sometime during August 10–31, 2020, except those in the two largest school districts, Broward and Miami-Dade, which began remote learning in August but did not resume in-person instruction until October 9 and November 10, respectively. Statewide, as of September 24, 45% of registered students received full-time in-person instruction.

To assess the occurrence of COVID-19 in Florida schools after resumption of in-person instruction, CDC and the Florida Department of Health (FDOH) reviewed school-related cases and outbreaks during August–December 2020. † County health department staff members conducted case investigations and contact tracing for all COVID-19 cases and reported data via the FDOH reportable disease surveillance system. A COVID-19 case was defined as nucleic acid amplification or antigen detection of acute infection with SARS-CoV-2 (the virus that causes COVID-19) in a symptomatic or asymptomatic person. A school-related case was defined as a COVID-19 case in a student or staff member who had been on campus for class, work, athletics, or other reasons during the 14 days preceding symptom onset or testing, and could reflect cases acquired in the school, home, or community setting. A school-based outbreak was defined as two or more epidemiologically linked school-related cases. Data regarding school start dates by district, student enrollment, and proportion of registered students receiving full-time in-person instruction were obtained from the Florida Department of Education. Information regarding temporary COVID-19–related school closures was obtained from FDOH staff members in the various counties. Data on school district mask use policies were obtained from reopening plans in each district ( 1 ). Descriptive statistics were computed; one-way analysis of variance and simple linear regression analyses were conducted to identify factors associated with student incidence by district. Statistical analyses were performed using JMP software (version 15.1; SAS Institute). This activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy. §

During August 10–December 21, 2020, a total of 63,654 cases of COVID-19 among persons aged 5–17 years were reported to FDOH; during the same period, 34,959 school-related COVID-19 cases were reported, including 25,094 (72%) among students and 9,630 (28%) among staff members. Therefore, among all cases reported among school-aged children, 39.4% were classified as school-related ( Figure ). School-related cases in children occurred in <1% (25,094 of 2,809,553) of all registered students. Among all cases in children aged 5–17 years, the median age was 13 years (interquartile range = 9–15 years) and did not differ between cases that were and were not school-related. Among school-related cases, 101 hospitalizations and no deaths were reported among students, and 219 hospitalizations and 13 deaths were identified among school staff members. Among the 13 staff members who died, nine had risk factors for severe outcomes, including obesity (seven), age >60 years (four), and other chronic conditions (four); some reported probable exposures outside the school setting, including within the household.

Contact tracing investigations identified 86,832 persons who had close school setting contact ¶ with persons with cases of school-related COVID-19; among these, 37,548 (43%) received testing. Overall, 10,092 (27% of contacts who were tested) received a positive SARS-CoV-2 test result while in quarantine. Testing of symptomatic persons was encouraged; however, 11% of school contacts who had COVID-19–symptoms** were not tested.

A total of 695 school-based outbreaks were identified in 62 (93%) of 67 school districts, involving 4,370 total cases, for a statewide average of 6.3 COVID-19 cases per outbreak. Therefore, <11% (695 of 6800) of schools reported an outbreak. A subset of 562 (81%) outbreaks with additional information was further analyzed; 110 (20%) of these outbreaks were associated with activities outside the classroom setting, including sports (91), nonschool–sponsored social gatherings (12), or transportation to school (four). The most frequent extracurricular sports-related outbreaks involved football (27), basketball (14), volleyball (nine), wrestling (eight), dance (eight), cheerleading (seven), and soccer (six). Sports-related outbreaks were larger on average than were nonsports–related outbreaks (mean = 6.0 cases versus 4.1 cases; p<0.01). The four largest sports-related outbreaks involved two wrestling events (58 and 27 cases) and two football events (18 and 17 cases). Most sports-related outbreaks involved high school grade levels.

Through December 18, 2020, a total of 28 schools in 12 counties closed temporarily because of COVID-19, with a median closure duration of 4 days (range = 1–14 days); 16 (57%) closures occurred in public schools, nine (32%) in private schools, and three (11%) in charter schools. Partial closures of one or more classrooms, but not the entire school, occurred in 226 schools in 38 counties; 88% of these partial closures occurred in public schools, 8% in private, and 4% in charter schools. Elementary school grades accounted for 75% of partial closures.

Descriptive statistics for the 67 county-based school districts indicated that a median of 70% of students were attending school and receiving full-time in-person instruction as of September 24 (range = <1% [Miami-Dade and Broward] to 94% [Baker]) ( Table 1 ). The median incidence among registered students was 1,280 per 100,000 students, ranging from 394 to 3,200 among counties.

Factors identified in bivariate analysis associated with student case rate by district were county population size, school opening during the first week, district reopening plans that included mandatory mask use, proportion of students attending in-person instruction, and the background case rate per county during August 10–December 21 ( Table 2 ). Higher mean student case rates were reported from counties with the lowest population, districts opening school during August 10–14, and districts that did not mandate mask use in their reopening plans, compared with rates in larger counties, districts opening after August 16, and those with mask mandates. The background cumulative disease incidence during August 10–December 21 in each county was positively correlated with the incidence among students. The proportion of students, by district, attending full-time in-person instruction also positively correlated with the student case rate. In general, smaller counties resumed classes earlier, had a higher proportion of students attending in-person instruction, were less likely to mandate universal mask use in schools, and had higher student incidences (2,212 per 100,000 in the lowest county population quartile versus 970 in the highest).

Although COVID-19 can and does occur in school settings, the results of these analyses indicate that in Florida, 60% of COVID-19 cases in school-aged children were not school-related, <1% of registered students were identified as having school-related COVID-19, and <11% of K–12 schools reported outbreaks. These findings add to a growing body of evidence suggesting that COVID-19 transmission does not appear to be demonstrably more frequent in schools than in noneducational settings ( 2 ). Temporal trends in the United States also indicate that among school-aged children, school-based transmission might be no higher than transmission outside the school setting ( 3 , 4 ); the limited in-school transmission observed in Florida has also been observed in other states ( 5 ) and countries ( 6 ).

Success in preventing the introduction of SARS-CoV-2 into schools depends upon controlling community transmission and adhering to mitigation measures in schools, particularly masking, physical distancing, testing, and increasing room air ventilation ( 2 , 4 , 7 ). Where feasible, supporting family choice for remote versus in-person learning likely reduces in-school crowding and facilitates better physical distancing in schools. In Florida, a large proportion of school-related outbreaks was observed among social gatherings and extracurricular sporting activities. Household transmission and social gatherings might pose a higher risk for infection among school-aged children than does school attendance ( 8 ). School sports and other extracurricular activities in which masking and physical distancing are difficult or impossible to achieve should be postponed, particularly during periods of high community transmission ( 2 , 9 ).

The findings in this report are subject to at least six limitations. First, because data on the number of teachers and staff members statewide or by county were not available, rates of total school-related cases could not be calculated; instead, the number of student cases per 100,000 registered students was used. Second, screening testing was generally not done in most schools, therefore, asymptomatic infections might have been underascertained. Third, classification of school-related cases, contacts, and outbreaks was dependent on thorough case interviews and might have been incomplete, relative to the overall number of cases in school-aged children. Fourth, although the operational definition used for school-related cases was likely sensitive, it does not ensure that all persons with school-related cases acquired infection in the school setting because infections might have been acquired elsewhere. Fifth, limited data were available at the school district level on some mitigation measures, such as mask use in schools, so these mitigation measures could not be fully assessed. Finally, results should be interpreted with caution because most students in the largest school districts did not resume in-person education for the first part of the analysis period.

These findings provide further evidence that resumption of school can likely be achieved without the rapid disease spread observed in congregate living facilities or high-density worksites. Both community-level and school-based measures to prevent spread of disease are essential to reduce SARS-CoV-2 transmission in school settings ( 10 ).

Acknowledgments

Florida Department of Health County Health Department staff members; Florida Department of Education; Leah Eisenstein, Amy Bogucki, Karla Bass, Judith Soteros, Geb Kiros, Florida Department of Health.

Corresponding author: Timothy Doyle, [email protected] .

1 Division of Disease Control and Protection, Florida Department of Health; 2 Division of State and Local Readiness, Center for Preparedness and Response, CDC; 3 Division of Community Health Promotion, Florida Department of Health; 4 Florida Department of Health.

All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Katherine Kendrick reports that she was an employee of the Florida Department of Health during the conduct of this study and currently is employed by Atrium, a Pfizer contractor. No other potential conflicts of interest were disclosed.

* https://www.flgov.com/wp-content/uploads/orders/2020/EO_20-52.pdf

† The last school day before Christmas break was December 18 in most districts; however, cases reported through December 21 were included to allow for testing and reporting time lag.

§ 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d); 5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 3501 et seq.

¶ Close contact is defined as contact within 6 feet of a person with a case of COVID-19 for ≥15 minutes, within a 24-hour period.

** https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html

Florida Department of Education. Coronavirus (COVID-19). Tallahassee, FL: Florida Department of Education; 2021. http://www.fldoe.org/em-response/index.stml
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Leeb RT, Price S, Sliwa S, et al. COVID-19 trends among school-aged children—United States, March 1–September 19, 2020. MMWR Morb Mortal Wkly Rep 2020;69:1410–5. https://doi.org/10.15585/mmwr.mm6939e2 PMID:33001869
Leidman E, Duca LM, Omura JD, Proia K, Stephens JW, Sauber-Schatz EK. COVID-19 trends among persons aged 0–24 years—United States, March 1–December 12, 2020. MMWR Morb Mortal Wkly Rep 2021;70:88–94. https://doi.org/10.15585/mmwr.mm7003e1 PMID:33476314
Zimmerman KO, Akinboyo IC, Brookhart MA, et al.; ABC Science Collaborative. Incidence and secondary transmission of SARS-CoV-2 infections in schools. Pediatrics 2021. Epub March 1, 2021. https://doi.org/10.1542/peds.2020-048090 PMID:33419869
Ismail SA, Saliba V, Lopez Bernal J, Ramsay ME, Ladhani SN. SARS-CoV-2 infection and transmission in educational settings: a prospective, cross-sectional analysis of infection clusters and outbreaks in England. Lancet Infect Dis 2021;21:344–53. https://doi.org/10.1016/S1473-3099(20)30882-3 PMID:33306981
Honein MA, Christie A, Rose DA, et al.; CDC COVID-19 Response Team. Summary of guidance for public health strategies to address high levels of community transmission of SARS-CoV-2 and related deaths, December 2020. MMWR Morb Mortal Wkly Rep 2020;69:1860–7. https://doi.org/10.15585/mmwr.mm6949e2 PMID:33301434
Hobbs CV, Martin LM, Kim SS, et al.; CDC COVID-19 Response Team. Factors associated with positive SARS-CoV-2 test results in outpatient health facilities and emergency departments among children and adolescents aged <18 years—Mississippi, September–November 2020. MMWR Morb Mortal Wkly Rep 2020;69:1925–9. https://doi.org/10.15585/mmwr.mm6950e3 PMID:33332298
Atherstone C, Siegel M, Schmitt-Matzen E, et al. SARS-CoV-2 transmission associated with high school wrestling tournaments—Florida, December 2020–January 2021. MMWR Morb Mortal Wkly Rep 2021;70:141–3. https://doi.org/10.15585/mmwr.mm7004e4 PMID:33507895
CDC. Operational strategy for K–12 schools through phased prevention. Atlanta, GA: US Department of Health and Human Services, CDC; 2021. https://www.cdc.gov/coronavirus/2019-ncov/community/schools-childcare/operation-strategy.html

FIGURE . Weekly school-related COVID-19 cases reported among students, as a proportion of overall cases in children aged 5–17 years and in the general population — Florida, August–December 2020*

* Week beginning December 21 is a partial week, only including December 21, 2020.


County characteristic	Median (range)
County population, all ages	130,642 (8,613–2,830,500)
Students enrolled in K–12 schools	15,306 (707–334,756)*
Students attending in-person full-time, median % (range)	70 (<1–94)

County incidence in general population	3,163 (1,915–14,606)
Incidence of school-related student cases among all registered students	1,280 (394–3,200)
School-related cases among students	170 (18–2,780)
School-related cases among staff members	68 (9–863)
Ratio of student to staff member cases	2.5 (1.1–7.4)
No. of school-based outbreaks**	5 (1–69)
No. of cases associated with school-based outbreaks	31 (2–541)

Abbreviation: K–12 = kindergarten through grade 12. * A total of 2,809,553 registered students were enrolled in approximately 6,800 public, charter, and private K–12 schools. † As reported by Florida Department of Education on September 24, 2020. § Total number of cases in the county during August 10–December 21, divided by county population, expressed per 100,000 persons. ¶ School-related cases in students by school district, during school start date and December 21, per 100,000 registered students (adjusted for school start date, i.e., adjusted rate = crude rate [131/x] where x = days from school start to December 18 and maximum number of days = 131). ** Two or more epidemiologically linked school-related cases.

December 21, 2020
Factor	Student rate*	P-value

Q1: 8,613–28,089	2,212	<0.0001
Q2: 28,090–130,642	1,430
Q3: 130,643–368,678	1,226
Q4: 368,679–2,830,500	970

August 10–14	1,882	0.01
After August 16	1,367	0.01

Yes	1,171	<0.01
No	1,667	<0.01
	R** = 0.5069	<0.0001
	R-squared = 0.2570	<0.0001
	R** = 0.4442	<0.001
	R-squared = 0.1973	<0.001

Abbreviations: Q = quartile; R = correlation coefficient. * School-related cases in students by school district, during school start date and December 21, per 100,000 registered students (adjusted for school start date: adjusted rate = crude rate[131/x] where x = days from school start to December 18 and maximum number of days = 131). † Sixty-seven Florida counties divided into four groups (quartiles) with quartile 1 containing the 17 counties with the lowest population per county, and quartile 4 containing the 16 counties with the highest population per county. Each of the other quartiles contains 17 counties. County population range of each quartile is specified next to each quartile designation. § Twenty-seven (40%) school districts had reopening plans requiring masks in schools. Inclusion in plan might not be an accurate reflection of mask use in school setting. ¶ Proportion of students attending full-time in-person instruction (continuous 0%–100%). ** R is a measure of correlation between the continuous independent variable indicated in the Factor column and the continuous dependent variable of student case rate per 100,000. R-squared indicates the percent of variation in student case rate that is explained by the independent variable included in the regression model. †† Per 100,000 population; excludes one outlier county (county with very small population and large outbreak in correctional facility, resulting in large county population rate with limited community spread).

Suggested citation for this article: Doyle T, Kendrick K, Troelstrup T, et al. COVID-19 in Primary and Secondary School Settings During the First Semester of School Reopening — Florida, August–December 2020. MMWR Morb Mortal Wkly Rep 2021;70:437–441. DOI: http://dx.doi.org/10.15585/mmwr.mm7012e2 .

MMWR and Morbidity and Mortality Weekly Report are service marks of the U.S. Department of Health and Human Services. Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of Health and Human Services. References to non-CDC sites on the Internet are provided as a service to MMWR readers and do not constitute or imply endorsement of these organizations or their programs by CDC or the U.S. Department of Health and Human Services. CDC is not responsible for the content of pages found at these sites. URL addresses listed in MMWR were current as of the date of publication.

All HTML versions of MMWR articles are generated from final proofs through an automated process. This conversion might result in character translation or format errors in the HTML version. Users are referred to the electronic PDF version ( https://www.cdc.gov/mmwr ) and/or the original MMWR paper copy for printable versions of official text, figures, and tables.

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https://educationhub.blog.gov.uk/2024/07/29/teacher-pay-everything-you-need-to-know-about-the-2024-pay-award/

Teacher pay: everything you need to know about the 2024 pay award

The Education Secretary has accepted the recommendations of the School Teachers’ Review Body (STRB) and confirmed the teacher pay award for 2024-25, setting out what school teachers in England can expect to be paid next academic year.

The STRB is an independent group that makes recommendations on the pay of teachers in maintained schools in England and reports to the Secretary of State for Education and the Prime Minister.

Each year the STRB recommends a pay award based on different factors including the economy, school workforce data and evidence from organisations including the DfE, and the teaching unions.

The government then considers the recommendations in depth and makes a decision on what pay award teachers receive for the coming year.

Here’s everything you need to know about teacher pay.

Are teachers getting a pay rise this year?

The STRB recommended a pay award of 5.5% and this has been accepted in full by the Education Secretary, reflecting the vital contribution teachers make to children’s life chances.

The 5.5% award would see pay packets increase by over £2,500 for the average classroom teacher, which would take the median salary for 2024/25 to over £49,000 a year.

Will teachers at all schools receive the pay award?

The pay award applies to maintained schools, with academies continuing to have freedom over their pay and conditions.

However, in practice most academies follow the recommendations of the STRB.

Is the pay award fully funded?

Yes. Schools will receive £1.1 billion in additional funding to cover their overall costs in financial year 2024-25, including fully funding the pay award for teachers at a national level.

This matches what we have calculated is needed to fully fund the teacher pay award and the support staff pay offer, at the national level, on top of the available headroom in schools’ existing budgets.

We are also providing an additional £97 million for schools delivering post-16 education (£63 million) and early years (£34 million) provision.

Taken together, this is an increase of almost £1.2 billion.

The pay award impacts both financial years 2024-25 and 2025-26. This additional funding only covers the financial year 2024-25 portion of the award. We will take into account the impact of the full year's costs of the teacher pay award on schools when considering 2025-26 budgets, which are yet to be agreed.

When will teachers receive their pay rise?

Teachers will start receiving their new salary in the autumn, after a new pay order is laid in Parliament and comes into force.

Pay will be backdated to 1 September 2024.

Will school support staff get a pay rise?

The teacher pay award only applies to school teachers, but the additional funding schools will receive also ensures schools are, at a national level, covered for the current 2024-25 pay offer for support staff, which is currently under negotiation.

Unlike teachers, most school support staff are currently employed on the pay and conditions of the National Joint Council (NJC) for Local Government Services. The NJC is a negotiating body made up of representatives from trade unions and local government employers.

We are committed to reinstating the School Support Staff Negotiating Body to give support staff like teaching assistants, caretakers and cleaners a stronger voice in government. The body will be tasked with establishing a national terms and conditions handbook, training, career progression routes, and fair pay rates for support staff.

What else are you doing to ensure teaching is an attractive profession?

Alongside the pay award, we have also announced that from September, schools will no longer be required to use the Performance Related Pay (PRP) system, which can lead to schools and teachers going through an overly bureaucratic process to agree individual teachers’ pay rises. This will help improve teacher workload.

We will also clarify that teachers can carry out their planning time at home, improving flexible working for staff.

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ORIGINAL RESEARCH article

The effect of convalescent plasma therapy on the rate of nucleic acid negative conversion in patients with persistent covid-19 test positivity.

1 School of Pharmaceutical Sciences, Shandong University of Traditional Chinese Medicine, Jinan, China
2 Phase I Clinical Trial Ward, The Fifth Medical Center of Chinese the PLA General Hospital, Beijing, China
3 Treatment and Research Center for Infectious Diseases, The Fifth Medical Center of PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing, China
4 Department of Gastroenterology of Chinese PLA General Hospital, Beijing, China
5 Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing, China
6 Respiratory Department No. 960 Hospital, The PLA, Jinan, China

Objective: This study investigates the association between convalescent plasma therapy and the negative conversion rate in patients with persistent COVID-19 test positivity.

Method: A retrospective analysis was conducted on patients with severe or mild to moderate COVID-19 whose viral nucleic acid tests remained positive for over 30 days. Patients were categorized into two groups: those who administered convalescent plasma therapy and those who were not. Data collected included information on therapy strategies used (convalescent plasma, corticosteroids, interferons, etc.), patients’ demographic characteristics, comorbidities, therapeutic medications, and nucleic acid testing results. Patients in the convalescent plasma therapy group were matched 1:2 ratio with those in the non-convalescent plasma therapy group. Cumulative negative conversion rates on the fifth, tenth, and fifteenth days post-therapy initiation were analyzed as dependent variables. Independent variables included therapy strategies, demographic characteristics, comorbidities, and therapeutic medication usage. Univariate analysis was conducted, and factors with a p -value ( P ) less than 0.2 were included in a paired Cox proportional hazards model.

Results: There was no statistically significant difference in the cumulative negative conversion rate between the convalescent plasma therapy group and the non-convalescent plasma therapy group on the fifth, tenth, and fifteenth days. Specifically, on day the fifth, the negative conversion rate was 41.46% in the convalescent plasma therapy group compared to 34.15% in the non-convalescent plasma therapy group (HR: 1.72, 95% CI: 0.82–3.61, P = 0.15). On the tenth day, it was 63.41% in the convalescent plasma therapy group and 63.41% in the non-convalescent plasma therapy group (HR: 1.25, 95% CI: 0.69∼2.26, P = 0.46). On the fifteenth day, the negative conversion rate was 85.37% in the convalescent plasma therapy group and 75.61% in the non-convalescent plasma therapy group (HR: 1.19, 95% CI: 0.71–1.97, P = 0.51).

Conclusion: Our finding does not support the hypothesis that convalescent plasma therapy could accelerate the time to negative conversion in patients who consistently test positive for COVID-19.

1 Introduction

Coronavirus disease 2019 (COVID-19) is a severe atypical respiratory infection first reported in Wuhan, China, in December 2019. COVID-19 is an infectious disease caused by the coronavirus COVID-19, which can cause a variety of respiratory diseases, colds, fever, and other symptoms, with a high infection rate, rapid mutation rate, low blood oxygen in the human body ( Ar Gouilh et al., 2018 ; Gattinoni et al., 2021 ; Ochani et al., 2021 ; Rahman et al., 2021 ). The disease has rapidly spread around the world, causing a great impact on the global economy and serious damage to human health, resulting in millions of confirmed cases and hundreds of thousands of deaths ( Gavriatopoulou et al., 2021 ).

In the face of the sudden onset of COVID-19, clinicians have adopted a variety of aggressive therapy strategies, including the use of Chinese medicine, thymosin, interferon, and convalescent plasma therapy ( Dai et al., 2020 ; Chen et al., 2021 ; Huang et al., 2021 ; Smith et al., 2022 ; Bellet et al., 2023 ). Convalescent plasma therapy is an important means of therapy that has many benefits for patients, can effectively shorten the discharge time and improve the symptoms of patients ( Janiaud et al., 2021 ). In the previous outbreak of severe acute respiratory syndrome (SARS) caused by SARS-CoV-1 coronavirus, studies have shown that convalescent plasma therapy can effectively shorten the course of disease, accelerate recovery, and reduce mortality ( Zhou X. et al., 2003 ; Berger et al., 2004 ; Cheng et al., 2005 ).

At present, there have been several clinical studies on convalescent plasma therapy, and there have been some controversies. For example, some studies have found that there is no significant correlation between convalescent plasma therapy and the mortality of COVID-19 patients, and the survival and discharge rates of patients in the convalescent plasma therapy group and the non-convalescent plasma therapy group are similar ( RECOVERY Collaborative Group, 2021 ). However, one study found that convalescent plasma therapy can effectively reduce the mortality of patients ( Liu Z, 2020 ), and another study has shown that convalescent plasma therapy can improve the rate of negative conversion in COVID-19 patients over 60 days ( Duan et al., 2022 ; Pan et al., 2022 ). Therefore, we conducted this study to investigate the relationship between convalescent plasma therapy and the viral clearance rate in patients with prolonged COVID-19 positivity.

2 Materials and methods

2.1 research overview.

This study included all patients with severe or mild to moderate COVID-19 diagnosed in 2020 at Huo Shenshan Hospital and Taikang Tongji Hospital in Wuhan, Hubei Province, China. It has been reviewed and approved by the Medical Ethics Committee of the Fifth Medical Center of the PLA General Hospital (approval number: 2020075D).

2.2 Inclusion criteria

(1) Age >18 years old, gender unlimited; (2) All patients included in the study met the diagnostic criteria for COVID-19 ( Lin and Li, 2020 ); (3) Severe or mild to moderate cases, the manifestations of fever and respiratory symptoms, imaging findings of pneumonia; (4) Nucleic acid positive duration more than 30 days; (5) Nucleic acid testing was performed every 1–3 days after 30 days in all patients to determine whether the nucleic acid turned negative; (6) COVID-19 detection is to extract nucleic acid from specimens using an automatic nucleic acid extraction instrument (KingFisher Flex, Thermo Company), collect purified nucleic acid for real-time fluorescent RT-PCR detection, and the period threshold (CT) value of 40 or greater is considered negative; (7) All patients had blood routine and biochemical examination records for 30 ± 3 days.

2.3 Exclusion criteria

(1) Patients with malignant tumors and malignant blood diseases; (2) Acquired immune deficiency syndrome (AIDS) patients; (3) Patients with liver failure or renal failure; (4) Patients with incomplete clinical information (see “ Section 2.5 ”) ( Lin and Li, 2020 ); (5) Other diseases that researchers believe may affect this study, such as lupus erythematosus.

2.4 Standard for negative conversion

Refer to “Diagnosis and Therapy Protocol for COVID-19 (Trial Fifth Revised Version)”, and negative nucleic acid tests of respiratory pathogens were performed two consecutive times with a time interval of 24 h or more.

2.5 Clinical Information collection

The following data were obtained from clinical records: (1) demographic data (gender, age); (2) concomitant diseases (such as diabetes, hypertension, cardiovascular and cerebrovascular diseases, respiratory diseases, chronic liver diseases, hematopoietic diseases, etc.); (3) The status of therapeutic drugs; (4) nucleic acid test negative time (see negative criteria); (5) 30 ± 3 days blood routine and blood biochemical test results; (6) Other therapy strategies.

2.6 Case matching method

The convalescent plasma therapy group and the non-convalescent plasma therapy group were matched in ratio of 1:2 by therapy time (date of convalescent plasma therapy minus date of onset). The matching method was as follows: The convalescent plasma therapy group was sorted according to the time interval between onset and convalescent plasma therapy. Patients in the convalescent plasma therapy group with the longest time interval between the therapy date and the onset date were first selected, and the patients with a negative conversion time longer than this interval were chosen from the non-convalescent plasma therapy group, and twice as many patients from the non-convalescent plasma therapy group over this were randomly selected for matching. The remaining patients entered the matching process with the second patient in the convalescent plasma therapy group, and so on. Until the last patient in the convalescent plasma therapy group was matched.

2.7 Statistical analysis

They were divided into two groups: the convalescent plasma therapy group, which received convalescent plasma therapy, and the non-convalescent plasma therapy group, which did not receive convalescent plasma therapy. The baseline conditions of the two groups were analyzed. mean ± SD was used to represent measurement data when they met normality, and a T-test was used for comparison between groups. When the measurement data did not conform to normality, the median (IQR) was used, and the Wilcoxon rank sum test was applied for inter-group comparison. The patients were grouped according to whether they turned negative on the fifth day, tenth day, or fifteenth day after receiving convalescent plasma therapy, and univariate analysis was conducted to explore the relationship between the respective variables and the dependent variables under univariate conditions and to provide a basis for the selection of independent variables. The statistical analysis method is the same as above. With whether or not the nucleic acid turned negative on the fifth day, tenth day, and fifteenth day as the dependent variable, variables with P < 0.20 in the above univariate analysis were selected as independent variables. The Cox proportional risk model was used to analyze the relationship between convalescent plasma therapy and nucleic acid turning negative after adjusting for the influence of other independent variables. All the above statistical analysis processes were completed based on SAS 9.4, and both were adopted by a two-sided test (α = 0.05).

The initial sample size was 3,000 patients. After excluding the patients without 30 ± 3 days of routine blood and biochemical examination records and the therapy time was less than 30 days, a total of 232 patients meeting the inclusion criteria were enrolled in the study. Excluding tumor patients and 12 patients with inaccurate specific onset time, 219 patients met the requirements of this study, of which 41 patients received convalescent plasma therapy. After 1:2 matching, 123 patients were finally included, including 41 patients in the convalescent plasma therapy group and 82 patients in the non-convalescent plasma therapy group (see Figure 1 ).

Figure 1 . The process of case matching.

3.1 Baseline data of patients in the convalescent plasma therapy group and non-convalescent plasma therapy group

The results showed that there were significant differences in platelet (PLT, P = 0.01), diabetes ( P = 0.003), cardiovascular and cerebrovascular diseases ( P = 0.04), interferon use (INF, P = 0.009), and glucocorticoid therapy ( P = 0.03) between the two groups, suggesting that the baseline of the two groups was unbalanced (see Table 1 ).

Table 1 . Baseline data of patients treated with and without therapeutic convalescent plasma ( N = 123).

3.2 Univariate analysis related to negative nucleic acid transformation on the fifth day, tenth day, and fifteenth day

The results showed that when the fifth-day cumulative conversion rate was used as the dependent variable, the independent variables with P < 0.20 were neutrophil (NE, P = 0.17), Glutamic-pyruvic transaminase (ALT, P = 0.19), Glutamic oxalacetic transaminase (AST, P = 0.07) and cardiovascular and cerebrovascular diseases ( P = 0.11). When the tenth day cumulative conversion rate was used as the dependent variable, the independent variables with P < 0.20 were sex ( P = 0.14), hemoglobin (HGB, P = 0.19), Glutamic-pyruvic transaminase (ALT, P = 0.11) and the ratio of albumin to globulin (A/G, P = 0.17). On the fifteenth day, when cumulative conversion rate was a factor variable, the self-variability of P < 0.20 was HGB ( P = 0.18) and direct bilirubin (DBIL, P = 0.17) (see Table 2 ).

Table 2 . Univariate analysis of negative transformation on the fifth day, tenth day, and fifteenth day.

3.3 Multi-factor analysis related to negative nucleic acid transformation on the fifth day, tenth day, and fifteenth day

The Cox proportional risk model was applied to stratify the matched groups, with cumulative conversion rate on the fifth day as the dependent variable, and indexes with P < 0.20 in Table 2 , including NE, ALT, AST, cardiovascular and cerebrovascular diseases, as the adjusting variables. There was no statistical significance between the convalescent plasma therapy group and the non-convalescent plasma therapy group (HR: 1.72, 95% CI: 0.82–3.61, P = 0.15). With the cumulative conversion rate on the tenth day as the dependent variable and the indexes with P < 0.20 in the results in Table 2 , including HGB, ALT, A/G, and gender as the adjusting variables, there was no significant statistical significance between the convalescent plasma therapy group and the non-convalescent plasma therapy group (HR: 1.25, 95% CI: 0.69–2.26, P = 0.46); With the cumulative conversion rate on the fifteenth day as the dependent variable and the indexes with P < 0.20 in the results of Table 2 , including HGB and DBIL as the adjusted variables, there was no significant statistical significance between the convalescent plasma therapy group and the non-convalescent plasma therapy group (HR: 1.19, 95% CI: 0.72–1.97, P = 0.51) (see Table 3 ).

Table 3 . Multivariate Cox analysis of influencing factors related to the fifth day, tenth day, and fifteenth day cumulative conversion rate.

3.4 Comparison of Kaplan-Meier curve of negative conversion rate between the two groups

The Kaplan-Meier curve of negative conversion rate between the convalescent plasma therapy group and the non-convalescent plasma therapy group showed that log-rank = 0.35, P = 0.56 cumulative conversion rate on the fifth day was taken as the dependent variable. When the cumulative conversion rate on the tenth day was taken as the dependent variable, log-rank = 0.00, P = 0.99; When the cumulative conversion rate on the fifteenth day was taken as the dependent variable, log-rank = 0.59, P = 0.44. These results showed that there was no statistical significance in Kaplan-Meier negative conversion rate curves between the convalescent plasma therapy group and the non-convalescent plasma therapy group on day 5, day 10, and day 15 (see Figure 2 ).

Figure 2 . Kaplan-Meier curve of negative conversion rate in two groups.

4 Discussion

The COVID-19 pandemic represents one of the most significant public crises in recent years. Determining the efficacy of convalescent plasma therapy in converting consistently COVID-19 positive patients to negative status is of paramount importance. To address this issue, we conducted a retrospective analysis and performed a cohort study on two groups of patients: those who received convalescent plasma therapy and those who did not receive convalescent plasma therapy. The convalescent plasma therapy group and the non-convalescent plasma therapy group were matched in 1:2 by therapy time (date of convalescent plasma therapy minus date of onset). The matching method was as follows: The convalescent plasma therapy group was sorted according to the time interval between onset and convalescent plasma therapy. Patients in the convalescent plasma therapy group with the longest time interval between the therapy date and the onset date were first selected, and the patients with a negative conversion time longer than this interval were chosen from the non-convalescent plasma therapy group, and twice as many patients from the non-convalescent plasma therapy group over this were randomly selected for matching. The remaining patients entered the matching process with the second patient in the convalescent plasma therapy group, and so on. Until the last patient in the convalescent plasma therapy group was matched. Initially, we performed univariate analysis on all independent variables. Factors with a P < 0.20 from the univariate analysis were then included in the Cox proportional hazards model for multivariate analysis. The results showed that after adjusting for other factors (NE, ALT, AST, cardiovascular and cerebrovascular diseases), we still did not find a statistical difference between therapy with convalescent plasma and therapy without convalescent plasma.

Our study does not support the association between convalescent plasma therapy and acceleration of SARS-CoV-2 conversion among COVID-19 patients with persistent positive RT-PCR tests. During the 2002 outbreak of Severe Acute Respiratory Syndrome (SARS), convalescent plasma therapy was shown to significantly facilitate the conversion of patients who continued to test positive for the SARS virus to negative ( Berger et al., 2004 ). Therefore, during the current COVID-19 pandemic, many clinicians have also employed convalescent plasma therapy to expedite viral clearance in patients. Recent years have seen numerous randomized clinical trials indicating that convalescent plasma therapy shows no significant association with clinical outcomes (Discharge, death, etc.) in patients with persistent positive COVID-19 test results ( Agarwal et al., 2020b ; Li et al., 2020 ; AlQahtani et al., 2021 ; Balcells et al., 2021 ; O’Donnell et al., 2021 ; Simonovich et al., 2021 ; Iannizzi et al., 2023 ). This aligns with the findings in the WHO COVID-19 Therapy Guidelines regarding convalescent plasma therapy ( Agarwal et al., 2020a ). However, these studies have not specifically investigated whether convalescent plasma therapy is related to the transition to negative test results in these patients. Our study further supplemented the evidence, demonstrating that convalescent plasma therapy is ineffective in accelerating viral clearance in patients who remain persistently positive for COVID-19. This finding is significant for the therapy of COVID-19 patients, as it suggests that convalescent plasma may not effectively shorten the recovery period or promote patient recuperation. We recommend incorporating these findings into current therapy protocols, which may lead clinicians to use convalescent plasma prudently when treating patients with persistent COVID-19 test positivity, not only to avoid the risk of donation from donors, but also to reduce the risk of ineffective therapy for patients.

Previous research on convalescent plasma therapy for COVID-19 has highlighted its potential benefits and limitations, warranting further investigation into its clinical efficacy and safety. Some studies have shown that administering convalescent plasma from recovered patients to severely ill individuals can rapidly provide immunoglobulins, allowing for timely therapy and potentially becoming an important therapeutic measure, especially for those with compromised immunity ( Ouyang et al., 2020 ; Senefeld et al., 2023 ). Additionally, early-stage administration of convalescent plasma therapy has been suggested to offer clear clinical benefits due to the rapid viral replication and high viral load in patients ( Sun et al., 2020 ). Our data included both severe patients with COVID-19 and those with early-stage COVID-19 infection, but in neither case did convalescent plasma therapy demonstrate a positive effect on clinical outcomes. Moreover, convalescent plasma therapy remains an “experimental therapy” in clinical practice ( Zeller et al., 2015 ). Its composition, which includes various components, can lead to serious medical complications such as allergic reactions in recipients ( Shu et al., 2021 ; Wood et al., 2021 ; Sullivan et al., 2022 ; Senefeld et al., 2023 ). Compared to small molecule drugs, convalescent plasma therapy is less safe and lacks sufficient clinical research ( Chai et al., 2020 ). Therefore, extensive prospective clinical trials are necessary to further explore its efficacy and safety.

The advantage of this study is that we used data from Wuhan, where all COVID-19 patients were systematically and centrally isolated and treated, and comprehensive patient data were obtained. Each patient was continuously observed until they tested negative for nucleic acid and discharged, providing a complete and continuous dataset from admission to discharge. However, this study also has several limitations: 1) Although the initial sample size was large, with over 3,000 cases, only 219 cases met the study criteria, and after matching, only 123 cases were included, making it a small retrospective cohort study; 2) Due to the inherent limitations of retrospective studies, the measured factors included in the analysis may not be complete, and some unknown factors may lead to bias in the results.

5 Conclusion

This study does not support that convalescent plasma therapy is associated with acceleration of negative conversion in COVID-19 patients with persistent positive RT-PCR tests.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Ethics statement

Ethical review and approval was obtained from the Medical Ethics Committee of the Fifth Medical Center of the PLA General Hospital (Approval number: 2020075D) for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the patients/participants OR patients/participants legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author contributions

YxW: Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal Analysis, Data curation, Conceptualization, Writing–review and editing, Writing–original draft. ZX: Writing–review and editing, Project administration, Formal Analysis. XX: Writing–review and editing, Methodology, Data curation. SY: Writing–review and editing, Software, Investigation, Conceptualization. YL: Writing–review and editing, Software, Investigation. HZ: Writing–review and editing, Investigation, Conceptualization. YZ: Writing–review and editing, Software, Data curation. FW: Writing–review and editing, Data curation. YnW: Supervision, Writing–review and editing. JB: Writing–review and editing, Supervision, Funding acquisition.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study is funded by the National Key Research and Development Program of China, China (project number: 2020YFC0860900).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: novel coronavirus pneumonia, convalescent plasma therapy, COVID-19 nucleic acid turned negative, negative conversion rate, retrospective analysis, multifactor analysis

Citation: Wang Y, Xu Z, Xu X, Yang S, Li Y, Zhang H, Zhang Y, Wang F-S, Wang Y and Bi J (2024) The effect of convalescent plasma therapy on the rate of nucleic acid negative conversion in patients with persistent COVID-19 test positivity. Front. Pharmacol. 15:1421516. doi: 10.3389/fphar.2024.1421516

Received: 22 April 2024; Accepted: 17 July 2024; Published: 01 August 2024.

Reviewed by:

Copyright © 2024 Wang, Xu, Xu, Yang, Li, Zhang, Zhang, Wang, Wang and Bi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ying Wang, [email protected] ; Jingfeng Bi, [email protected]

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Analyzing the impact of COVID-19 on the grades of university education: A case study with economics students

Juan ferrer.

a Department of Agricultural Economics, Statistics and Business Management, ETSIAAB, Universidad Politécnica de Madrid, Campus Ciudad Universitaria, Av. Puerta de Hierro 2-4, 28040, Madrid, Spain

Eva Iglesias

b CEIGRAM, Universidad Politécnica de Madrid, Senda del Rey 13, 28040, Madrid, Spain

Irene Blanco-Gutiérrez

Julio estavillo, associated data.

Not applicable.

COVID-19 has been one of the major incidents in the global university education system in recent years. Its influence and effects on education are still difficult to determine today. Both students and teachers have had to change their study and work routines and disciplines, in many cases lacking the necessary infrastructure to adapt to online learning. Students had to start a new academic year with a complete return to face-to-face teaching without having overcome, in many cases, the incidence of online learning. This study, through 167 responses to a survey addressed to economics students at the Universidad Politécnica de Madrid, aims to analyse the causes of an improvement or a worsening of the academic performance of university students in the return to normality after having gone through COVID-19's restrictions. The results obtained show that students, students who attend tutorials and those who have evaluated online teaching positively, are the ones who have most improved their performance in the return to face-to-face teaching. And those who have suffered the physical and psychological consequences of COVID and those with less infrastructure and income have worsened their results.

1. Introduction

Spain forced the closure of all universities and education centres on March 12, 2020. A few days later, on March 14, 2020, the State of Alarm was declared, and strict home confinement of the entire population was imposed, except for those related to essential services. The global pandemic of COVID-19, declared on 11 March 2020, was the cause of the radical change in education that students and teachers had to face ( BOE, 2020 ; Camilleri, 2021 ; Díez-Gutiérrez & Gajardo-Espinoza, 2020 ).

The absence of students from the classrooms and the start of distance and online teaching affected the end of the academic year 2019/2020, and in most cases continued into the academic year 2020/2021. During this time, the universities and their teaching staff had to continue learning with the means at their disposal and in most cases without having gone through learning courses or adaptation of the subjects and content ( Camilleri, 2021 ; Trujillo, 2020 ).

The lockdown and rapid transformation of educational activity had a severe impact on students and their learning process ( Odriozola-González et al., 2020 ). Different studies have reported on the difficulties and stress faced by students and teachers during this time of online teaching ( Camilleri, 2021 ; Díez-Gutiérrez & Gajardo-Espinoza, 2020 ; Tejedor et al., 2020 ). The lack of social contact with peers and the difficulty of sharing knowledge and educational experiences profoundly altered university learning environments. In a similar vein, the difficulty of ensuring that all students have access to the same information under the same conditions became particularly evident ( Bekerman & Rondanini, 2020 ). Some studies also suggest that the pressure on university students has been significant, but its effects have differed according to socioeconomic backgrounds ( Odriozola-González et al., 2020 ). Students who did not have sufficient computer equipment, access networks or a non-shared room where they could receive telematic classes with the necessary privacy were the most affected by the confinement ( Álvarez, 2020 ).

After the end of the state of alarm established by RD 463/2020, and the improvement of general parameters during the summer of 2020, the increase in cases made it necessary for the Government to declare a new state of alarm by means of RD 926/2020 dated October 25th, 2020, which was extended until May 8th, 2021 (Royal Decree-Law 8/2021, of 4 May). With the end of the state of alarm, which would hardly have any effect on the 20/21 academic year, and the improvement of the parameters related to the evolution of the pandemic, the plans for a return to face-to-face teaching in Spanish universities were slowly initiated. In the case of the Community of Madrid, on which the Universidad Politécnica de Madrid (UPM) depends, the academic year started on September 7th with a hybrid learning approach (online and face-to-face classes). The return to full face-to-face attendance took place only one month later, on October 4th, 2021.

Once again, there was a sudden shift to face-to-face learning and a mismatch between teaching programmed for online attendance and classroom attendance. As a result, teachers and students suffered a new setback caused by changes in teaching parameters and methods. In some cases, they returned to pre-confinement teaching, but in others they incorporated tools already used in the confinement period, which had to be modified to ensure distances and social collective security parameters ( Aretio, 2021 ).

The cumulative impact of the COVID-19 pandemic on students' academic achievement has been large, but underexplored ( Borgaonkar et al., 2021 ; Clark et al., 2021 ).This paper attempts to fill this gap by sheding light on how the COVID-19 pandemic has affected the academic performance of university students in the return to face-to-face learning. Based on a case study with economics students at the Universidad Politécnica de Madrid, the article analyses which factors have determined better and worse performance in students’ grades.

This paper is articulated as follows: in section 2 , the literature and hypotheses are studied; in section 3 , materials and methods; in section 4 , results; and finally, the discussion and conclusions are addressed in the last two points.

2. Literature review and hypotheses

The term ‘academic performance’ is often debated due to its multidimensional nature. However, for operational reasons, it is generally accepted that the grades obtained in the subjects taken at university are the best indicator of students' academic performance ( Araya-Pizarro & Avilés-Pizarro, 2020 ; Fernandez-Mellizo & Constante-Amores, 2020 ; Mora-García, 2015 ; Tomás-Miquel et al., 2014 ).

Numerous studies have analyzed the factors that influence the academic performance (grades) of university students [e.g., 12-16-17]. These studies, carried out in more stable environments than those conditioned by the COVID-19 crisis, reveal a series of factors that may have an impact on the differential performance of students. Mora-García ( Mora-García, 2015 ) distinguishes between psychosocial factors (personal characteristics of the student, motivation, anxiety, self-esteem, etc.) and socio-demographic factors (gender, family's economic level, employment situation, parents' level of education, etc.). Other authors [e.g., 12] consider demographic (gender and age), socio-economic (i.e., the socio-economic level of the family, which can have repercussions on having to combine work and school) and academic factors (access grade and type of school).

Although studies have been conducted by many authors, this problem is still insufficiently explored. Only few works have analyzed the impact of COVID-19 crisis on the grades of university students and their results are inconclusive ( Karadag, 2021 ). For example, Clark et al. ( Clark et al., 2021 ), found that students improved their grades during COVID-19 due the use of online systems. On the contrary, Borgaonkar et al. ( Borgaonkar et al., 2021 ), indicate a decline in performance, especially in first year engineering courses, because of a drop in class attendance and student motivation. It is necessary to comment on how an increase in the so-called “contract cheating” may have occurred during COVID_19, as this element has already been recognized in different studies [e.g. 19–21]. It is impossible to know the extent of this practice in the present study, although it is true that the students who were evaluated online signed a loyalty commitment before taking the tests. Although in this study, we focus on the return to face-to-face attendance when the possibility of cheating on exams became much more restricted, but this element could be considered a limitation of the study.

More importantly, to our knowledge, no previous study has analyzed how returning to the classroom have affected the academic performance of university students. Thus, this research addresses the gap that exist in terms of how academic performance has evolved in post-confinement and what are the differential elements that have caused some students to see their grades improve or worsen when they returned to face-to-face learning. This research contributes to the literature on the impact of the COVID-19 pandemic on students’ academic achievement and focuses on a little-studied period, the post-confinement. The study will serve as a basis for future research on the subject and will help education authorities to contextualize possible actions in the event of a pandemic.

In the analysis, we have assumed that the factors that affected students' performance during confinement continue to be decisive and have also influenced students’ academic performance upon return to the classroom. One of these factors is the socio-economic level of the students, which has been extensively referenced in several studies ( Aucejo et al., 2020 ; Betts & Morell, 1999 ; Cyrenne & Chan, 2012 ). These studies show that students with fewer resources have felt the impact of COVID-19 the most. Following Clark et al. ( Clark et al., 2021 ), our study analyses the impact of appropriate study space and technology on academic achievement.

Low economic status of students will influence students to have lower grades.

Other factor that is studied in this article is the student's perception on the adequacy of online teaching. Some authors indicate that certain elements used in online teaching (e.g., continuous tutoring and test-type evaluations instead of conventional exams) have contributed to improve the quality of learning and could be maintained in the return to face-to-face teaching ( Aretio, 2021 ; Clark et al., 2021 ; Yu et al., 2022 ). Our hypothesis is that those students who have better adapted to online teaching tools may have a greater chance of improving or maintaining their performance.

A positive adaptation of students to online learning will positively affect their grades.

In addition, the COVID period exposed students to stressful situations, in many cases due to an excessive workload that has hardly ever evaluated, nor has its influence on performance determined ( Beena & Sony, 2022 ; Nguyen et al., 2020 ; Yu et al., 2022 ).

Work overload experienced by students during COVID-19 will influence lower grades.

Thus, the physical, psycho-logical and emotional consequences of COVID-19 are key factors and have also included in our study. The most challenging aspect of learning during COVID-19 is the increase of reported cases of mental health issues in college students ( Ibarra-Mejia et al., 2021 ; Khobragade et al., 2021 ; Tubbs, 2021 ).

Those students who have suffered the physical and psycho-logical consequences of COVID-19 will obtain lower grades.

Finally, gender issues are examined. Here, two opposing effects must be considered. There is no consensus on the performance of men and women, while some argue that women get better grades ( Betts & Morell, 1999 ; Richardson & Woodley, 2003 ), other studies have not reached these conclusions and equalize the performance of both genders ( Alghamdi et al., 2020 ; Gestsdottir et al., 2021 ; Richardson & Woodley, 2003 ). On the other hand, COVID-19 has caused mental disorders and psychological problems for students due to stress and isolation, and these effects have been greater in women than in men ( Balderas & Caballero-Hernández, 2020 ; Gestsdottir et al., 2021 ). Although men have a greater tendency to abuse drugs and alcohol during periods of stress such as that experienced with COVID-19 ( Gestsdottir et al., 2021 ).

Female students will obtain similar grades to their male counterparts in the return to face-to-face training.

3. Materials and methods

3.1. sample.

In order to obtain information on the performance of university students in the return to face-to-face teaching, an on-line survey was conducted for some economic courses at the Universidad Politécnica de Madrid, which we present as a case study. The participants are studying economic subjects taught by the Department of Agricultural Economics, Statistics and Business Management. The study was carried out between October and December 2021, and 167 grade students participated. The students were informed of the ethics statement of the study, ensuring the non-use of data on an individual basis, always aggregated and ensuring the confidentiality of their responses, with no traceability of their participation (see Appendix I ).

The characteristics of the participants in the survey are summarized in Table 1 , Table 2 .

Characteristics of the sample in terms of gender.

Variable		N responses	Percentage of total
Gender	Female	77	46.1%
	Male	87	52.1%
	N/A	3	1.8%

Characteristics of the sample in terms of age, years at the UPM and nº of courses taken.

Variable	N	Mean	Min	Max	Standard dev.
Age	167	20.4	18	42	3.16
Years in university	167	2.28	1	5	1.11
Number of courses	167	6.05	1	10	1.29

The average age is around 21 years old, with slightly more than two years at the university, with around 6 courses at a time, and with a slightly greater preponderance of men than women.

3.2. Variables

A research question is posed: How have student's grades (dependent variable) evolved in the return to face-to-face teaching, and to what of the factors considered in the hypotheses could have affected their performance, namely, socioeconomic level, evaluation of online teaching, workload, having suffered the physical or psychological consequences of COVID, and being a woman. On the other hand, some control variables ( Benson & Brown, 2011 ) have been considered that can show other aspects not considered by the hypotheses, such as: attendance to tutorials before, during and on return, the realization of other activities outside the study, such as social actions before, during and on return, and written exams several authors have related tutorials and a better performance ( Binani & Chowdary, 2018 ; Bunce et al., 2017 ; John, 2005 ; Kramer et al., 2018 ) and related with worst performance number of courses ( Whitfield & Xie, 2002 ). Table 3 shows the variables used to test or reject the hypotheses.

Hypotheses and variables used to test.

Hypotheses	Variable (number)
H1: Low economic status of students will influence students to have lower grades.	Inadequate workplace (2), the workplace has not improved and was inadequate, see ( )
H2: A positive adaptation of students to online learning will positively affect their grades.	Online learning assessment (3)
H3: Work overload experienced by students during COVID-19 will influence lower grades.	Online workload (4)
H4: Those students who have suffered the physical and psycho-logical consequences of COVID-19 will obtain lower grades.	Physical and emotional consequences in COVID time (5)
	Physical and emotional consequences in recovery (6)
H5: Female students will obtain similar grades to their male counterparts in the return to face-to-face training.	Gender (7)
Control variables	Tutorial (8)
	Other Activities (9)
	Written exam (10)
	Number of courses (11)

Table 4 shows the descriptive statistics of the different variables, as well as their methodological definition. In Appendix I , the questionnaire and the questions that condition the different variables are shown, and the reference to previous studies that have been sources of the questionnaire used.

Descriptive statistics of the variables used.

Variable	Definition	N	Mean	Std. Dev.	Min	Max
Grades (1)	Student grades in the return to face-to-face teaching. Where 1 is much worse, and 5 is much better (Question 10 see ).	163	2.51	1.085	1	5
Inadequate Workplace (2)	Dummy variable that takes the value 1 if the workplace has not improved and was inadequate, and 0 otherwise (Question 7e, see ).	166	0.05	0.215	0	1
Online learning assessment (3)	Assessment of online versus face-to-face learning. Where 1 is much lower, and 5 is much higher (Question 11, see ).	162	2.24	1.168	1	5
Online workload (4)	Assessment of the workload of online versus face-to-face teaching. Where 1 is much lower, and 5 is much higher (Question 12, see ).	161	3.55	1.024	1	5
Physical and emotional consequences in COVID time (5)	Assessment of the physical and emotional consequences generated by confinement. Where 1 is very low and 5 is very high (Question 5a, see ).	166	3.47	1.189	1	5
Physical and emotional consequences in recovery (6)	Assessment of the physical and emotional Covid consequences in recovery. Where 1 is very low and 5 is very high (Question 5b, see ).	164	3.19	1.138	1	5
Gender (7)	Dummy variable that takes the value 1 if the student is defined as female, and 0 if the student is defined as male (Question 1, see ).	164	(see )
Tutorial (8)	Frequency with which the student has had tutorials before, during confinement and on return to face-to-face attendance. Where 1 is very low frequency, and 5 is very high frequency, for each period (Question 9 see , sum of 9a, 9b and 9c).	163	7.26	2.705	2	15
Other Activities (9)	Work, volunteering, or elderly care activities that the student has combined before, during and upon returning to the classroom (Question 6 see , sum of 6a, 6b and 6c).	167	1.24	1.652	0	9
Written exam (10)	Student assessment of written exams as main evaluation tool (Question 8, sum of the written exam, see )	167	2.24	0.830	0	3
Number of courses (11)	Number of courses the student has enrolled in (Question 4, see ).	166	6.05	1.290	1	10

Table 5 shows the correlations between the different variables observed. Tau B of Kendall's has been calculated as they are non-parametric variables.

Tau B of Kendall's correlation matrix.

Tau B of Kendall	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
Grades (1)	1.000
Inadequate workplace (2)	−.170*	1.000
Online learning asses. (3)	.166*	.050	1.000
Online workload (4)	−.121	.063	−.114	1.000
Physical and emotional conseq.in COVID (5)	−.279**	.067	−.266**	.195**	1.000
Physical and emotional conseq. in recovery (6)	−.225**	.163*	.104	.003	.037	1.000
Gender (7)	−.024	−.043	−.120	.068	.186**	−.029	1.000
Tutorial (8)	.136*	.126	.088	.050	.064	−.049	.166*	1.000
Other Activities (9)	.030	.055	.035	.063	−.042	.084	−.192**	.060	1.000
Written exam (10)	−.072	−.088	−.025	.037	.039	.017	.105	.081	.019	1.000
Number of courses (11)	−.048	.057	.095	.000	−.125	.057	−.232**	−.062	.029	−.165*	1.000

**. The correlation is significant at the 0.01 level (bilateral). *. The correlation is significant at the 0.05 level (bilateral).

3.3. Regression model

To assess the relationship between the independent variables and the dependent variable, an ordered logit regression is proposed. The objective is to determine the relationship between the explanatory (independent) variables X i and an ordinal response variable with g levels. The ordered logit model is derived from a latent variable model where Y* is an unobserved variable that, in our case, may reflect latent student's perceptions on the impact of COVID in their academic performance and can be related to the set of independent variables X i as expressed in equation (1) .

Where β is the vector of parameters to be estimated, ε is the random disturbance following normal distribution, ε∼N(0,1).

While Y* is unobserved, we can observe a response variable Y, in our case with 5 levels, and can define the following relationship between them. Let μ 1 < μ 2 < μ 3 < μ 4 be four unknown threshold or cut-off parameters, we assign five values to the observed response variable Y such that: Y = 1 if Y∗ ≤ μ 1 ; Y = 2 if μ 1 <Y∗ ≤ μ 2 ; Y = 3 if μ 2 < Y∗ ≤ μ 3 ; Y = 4 if μ 3 Y∗ ≤ μ 4 ; Y = 5 if μ 4 < Y*. The cumulative probability of a given variable Y is the probability that Y* is less than or equal to a given value g ( Harrell, 2001 ). Thus, taking into account that the dependent variable Y* represents the probability that the student obtains worst, somewhat worst, neutral, somewhat better or better grades, the ordered logistic model is defined as follows:

Where g = 1,2 … 5 represents the five possible values taken by the observed dependent variable. In our case, the explanatory variables are the variables number (2) to (11) defined in Table 3 , while the variable Y is the observed response variable (1) Grades, defined in five levels as shown in Table 3 (g = 1,2, …5). Substituting X for each of the independent variables, we have:

where the explanatory variables are Inadequate workplace ( X 1 ), Online learning asses ( X 2 ), Online workload( X 3 ), Physical and emotional consequences in COVID ( X 4 ), Physical and emotional consequences in recovery ( X 5 ), Gender( X 6 ), Tutorial( X 7 ), Other activities( X 8 ), Written exam ( X 9 ), Number of courses ( X 10 ), and ε is the random disturbance following normal distribution, ε∼N(0,1).

Table 6 shows the development of the ordered logistic regression model.

Logistic regression model.

Grades	Coef,	Std, Err	z	P > z	[95% Conf, Interval]
Inadequate workplace	−1.6853	0.7364	−2.29	0.022	−3.1287	−0.2419
Online learning assessment	0.3369	0.1415	2.38	0.017	0.0594	0.6143
Online workload	−0.1719	0.1454	−1.18	0.237	−0.4570	0.1131
Physical and emotional consequences in COVID	−0.6586	0.1421	−4.63	0.000	−0.9370	−0.3801
Physical and emotional consequences in recovery	−0.3500	0.1364	−2.57	0.010	−0.6173	−0.0827
Gender	0.4473	0.3181	1.41	0.160	−0.1761	1.0707
Tutorial	0.1211	0.0589	2.06	0.040	0.0057	0.2365
Other Activities	0.1269	0.0973	1.30	0.192	−0.0639	0.3177
Written exam	−0.0836	0.1262	−0.66	0.507	−0.3309	0.1636
Num. of courses	−0.2815	0.1204	−2.34	0.019	−0.5176	−0.0455
/cut1	−5.6845	1.2796			−8.1925	−3.1765
/cut2	−3.8684	1.2378			−6.2945	−1.4423
/cut3	−1.8960	1.2177			−4.2827	0.4907
/cut4	−0.1063	1.2376			−2.5319	2.3193
	Number of obs				165
	LR chi2 (10)				59.60
	Prob > chi2				0.0000
Log likelihood = −207.221	Pseudo R2				0.1257

The effects of the independent variables have been tested to ensure that they are the same depending on the level of the dependent variable, with the test of the parallel regression assumption, which is shown in Table 6 , allowing the ordered logistic regression model used in Table 7 to be validated.

Test of the parallel regression assumption.

Test	Chi2	Df	P > Chi2
Wolfe Gould	31.91	30	0.372
Brant	16.22	30	0.981
Wald	15.69	30	0.985

The results show that the probability of obtaining an improvement in the grades of university students in the return to face-to-face teaching is positively influenced by the positive evaluation of online learning assessment (P > z = 0.017 and Coef = 0.3369), the student's attendance to tutorials (P > z = 0.040 and Coef = 0.1211). With less significance, by being a woman (P > z = 0.160, out of the 95% of confident interval but inside the 80%, with a Coef of 0.4473) and other activities (P > z = 0.192, out of the 95% of confident interval but inside the 80%, with a Coef. of 0.1269). In addition, the factors that reduce the likelihood of performance improvement and cause lower performance are the physical and emotional consequences in COVID (P > z = 0.000 and Coef = −0.6586), the physical and emotional consequences in recovery (P > z = 0.010 and Coef = −0.35), inadequate workplace arrangements (P > z = 0.022 and Coef = −1.685), taking a greater number of subjects (P > z = 0.019 and Coef = −0.2815). Regarding the hypotheses put forward in this work, the results show the following conclusions: Hypothesis 1 is confirmed since having an inadequate workplace, linked to belonging to lower income groups ( Cyrenne & Chan, 2012 ), is related with worst performance. Hypothesis 2 on the adaptation and valuation of online teaching and its relationship with the improvement in grades is confirmed. Hypothesis 3 , on the negative effect of the workload during COVID-19 on grades, is rejected. Hypothesis 4 , on the negative effects of the physical and psychological effects of COVID-19 on grades, is confirmed. Finally, it is shown that the gender option does not have an influence on the grades, so hypothesis 5 is confirmed, although a higher confidence range, 80%, would allow to defend a better perceived performance of women in the return to presence. In the Fig. 1 , we resume the results of the study differentiating between negative and positive effects of the variables that have been found to be significant.

Variables with an effect on student grades.

5. Discussion

The crisis of the COVID-19 pandemic has undoubtedly had a severe influence on teaching throughout the academic world, as various studies have corroborated [e.g. 2–3, 6, 9, among others]. These studies have reported adverse impacts related to the lockout of COVID-19, from the stress suffered by students, the lack of adaptation of schools and universities, the lack of knowledge of students and teachers of the technologies and methodologies to be implemented ( Asgari et al., 2021 ; Bilen & Matros, 2021 ; Camilleri, 2021 ; Prowse et al., 2021 ), as well as factors of loneliness, incomprehension and isolation of students and teachers ( Clark et al., 2021 ; Rodríguez-Planas, 2022 ).

Once the initial phase of COVID-19 had passed, there was a return to face-to-face teaching. However, this return to normality has been tinged with the ink of the previous period. This has been visualized through different elements ranging from, the maintenance of social distance, the use of the masks, the fear of physical contact and proximity ( Camilleri, 2021 ; Ewing, 2021 ) and also the maintenance of routines and procedures introduced in the confinement period, such as online teaching, the increased use of test exams or video conferencing ( Aretio, 2021 ; Ewing, 2021 ; Zhao & Watterston, 2021 ).

Thus, students in the return to face-to-face learning are incorporated into an environment that is close to the previous one, but at the same time disconcerting and alien to them. Libraries are not used, classes become cold and extremely stuffy places with distant and distant teachers ( Camilleri, 2021 ; Lockee, 2021 ). This undoubtedly has an impact on the evolution of their performance, which is the gap that this article aims to fill. Since not all students are the same, and not all students have the same capacity for adaptation, what are the factors that facilitate students' improved performance in the return to face-to-face teaching and how has this been influenced by COVID-19?

We have analyzed the factors that have historically been related to student performance, such as gender, the number of subjects taken, and the socio-economic status (through the availability of a suitable workplace). In addition, other elements linked to the COVID-19 crisis have been incorporated into the study, such as the evaluation of online teaching, the psychological and emotional effects of COVID-19, workload, and the provision of appropriate technology has become a key element in the monitoring of courses ( Araya-Pizarro & Avilés-Pizarro, 2020 ; Lockee, 2021 ). With these variables affected by the COVID-19 pandemic and the direct changes undergone in the adaptation of university education, five working hypotheses have been formulated, and four control variables have been used ( Benson & Brown, 2011 ).

Although previous studies have addressed similar problems under different contexts, the results are complex to relate because of their novelty. The results obtained show that the positive evaluation of online teaching is the factor that most affect the improvement of student grades. Thus, our hypothesis 2 (students who have better adapted to online teaching tools may have a greater chance of improving or maintaining their performance) is confirmed. Along these lines, studies show that online teaching is not a complete substitute for face-to-face teaching. Asgari et al., [408], reveal that half of the students reported feeling tired and demotivated by video-conferencing classes. Also indicate that in the most disadvantaged neighborhoods there is a greater decrease in attendance in online classes: Although the sentiment is not unanimous, most students rate the online learning experience as positive and their rating increases in the year following the closure of the classrooms ( Martín-Núñez et al., 2022 ). Along the same lines, we have found no correlation between inadequate work environment, a variable related to low family income, and negative evaluation of online learning (see Table 5 ). Online teaching has led to an improvement in students' grades over those who did not receive online teaching, and students who took exams using a computer also perform better than those who did not take exams using this technology ( Clark et al., 2021 ).

Our study also demonstrates that the use of tutorials has contributed to improved grades. Previous studies have already noted the importance of class attendance and contact with the teacher, which was only possible through tutorials in the COVID-19 period ( Araya-Pizarro & Avilés-Pizarro, 2020 ; Béjar & Vera, 2022 ). The abrupt shift to remote learning due to the COVID-19 pandemic caused teachers to look for new types of evaluation methods. In many cases, traditional written exams were replaced by alternative assessment methods (e.g., computer-based tests, paper reviews, etc.) that were well received by students ( Borgaonkar et al., 2021 ; Clark et al., 2021 ; Sletten, 2021 ). García et al. ( García et al., 2022 ) indicate that students improved their grades by using computer tools in parallel to the usual paper-based exam technology.

The results obtained suggest that the impact of COVID-19 persists after returning to face-to-face teaching. Students who have endured psychological and physical consequences during COVID-19 and who had poor working facilities and technology had difficulty recovering performance.

Several authors have shown that students with fewer resources have felt the impact of COVID-19 the most. Aucejo et al. ( Aucejo et al., 2020 ) show that lower-income students are more likely to delay graduation than their higher-income peers because of COVID-19. Also, Rodriguez-Planas ( Rodríguez-Planas, 2022 ) highlights how the gap between social classes in the classroom has increased during and after COVID, as it has been the neediest students, who had to work in order to combine studies and work or who have lost their jobs due to COVID, which has further reduced their resources. This relates to the widely discussed digital divide, that in many cases is related to lower socio-economic status ( Zhao & Watterston, 2021 ). The results of this study support previous findings that inadequate workspace and technology lead to lower student performance ( Abu Talib et al., 2021 ; Di Pietro et al., 2020 ). Other elements that worsen academic performance in the post-confinement COVID period are taking a high number of subjects and being an undergraduate student. The increased workload of taking more subjects at the same time and the reduced university experience of undergraduates has an impact on students' grades ( Betts & Morell, 1999 ; Mora-García, 2015 ).

Finally, it is important to note that women have the perception of having improved their grades more than men in the return to face-to-face learning (80% of confident interval). Some studies revealed that women have suffered more than men the consequences of the COVID crisis and its psychological, emotional, and isolating effects ( Prowse et al., 2021 ). Therefore, it is possible that the end of this stressful situation has brought about a kind of release and, as a result, an improvement in their performance. This is a possibility that the authors consider possible, but that further studies should corroborate.

6. Conclusions

This study reveals that the COVID-19 effect persists and has negatively affected students' results. Students who have more difficulty adapting to new circumstances, such as those encountered in COVID-19, have poorer academic results. The ability to adapt to the new circumstances caused by the COVID-19 epidemic or the ability to have more resources available to adaptappears as an important factor. This element had already been suggested in other studies but not in the COVID-19 pandemic, and this is undoubtedly the novelty of this study. And ability to adapt has been shown in this study through various elements, adapting better to online teaching (coef = 0.33 and P > z = 0.017), attending, and using tutorials (coef = 0.12 and P > z = 0.04), having broader horizons (carrying out other activities) (coef = 0.13 and P > z = 0.19), and the complex and little studied gender effect (coef = 0.45 and P > z = 0.16). On the other hand, the lack of adaptation has specific causes, the COVID-19 disease condition (coef = −4.63 and P > z = 0.00), the availability of less financial means and taking more subjects than the average of the other students (coef = −0.28 and P > z = 0.02). Thus, not all social strata are equally affected by COVID in the return to face-to-face learning. Lower income strata of society have a worse performance in the return to face-to-face learning, and women improve their performance in the return to face-to-face learning, after having suffered more severely from the consequences of COVID. The technological gap appears as a key factor and becomes even more relevant as it highlights the importance of all students not having adequate equipment and environment to carry out their studies (coef = −1.68 and P > z = 0.02). This is especially important because the use of interactive technologies could become the norm, in a post COVID-19 era ( Borgaonkar et al., 2021 ; Camilleri, 2021 ).

The positive conclusions of this study focus on the factors that favor better student performance in the return to face-to-face teaching, adapting to new circumstances and environments is therefore vital. These include the integration of communication technologies in teaching, the importance of students' use of tutorials, and the development of multiple-choice tests ( Nallusamy & Punna Rao, 2018 ). This opens the door to the development of online teaching and courses, which will undoubtedly gain momentum after the COVID crisis ( Aretio, 2021 ; Díez-Gutiérrez & Gajardo-Espinoza, 2020 ; Lockee, 2021 ). This study presents a series of limitations, derived from the type of research, a subjective survey, and the scope of the study. Thus, the possibility of lack of rigor in the responses may be a limitation on the data obtained. On the other hand, the survey was carried out in Spain and at the Universidad Politécnica de Madrid, for students of economics courses. It is difficult to extrapolate these analyses to other universities and other academic environments and this is undoubtedly a limitation that should be supported by further research.

This research received no external funding.

Institutional review board statement

Data availability statement, credit authorship contribution statement.

Juan Ferrer: Conceptualization, Methodology, Software, Data curation, Writing – original draft, preparation, Visualization, Investigation, Supervision, Writing – review & editing. Eva Iglesias: Conceptualization, Methodology, Software, Data curation, Writing – original draft, preparation, Supervision, Software, Validation, Writing – review & editing. Irene Blanco-Gutiérrez: Conceptualization, Methodology, Software, Data curation, Writing – original draft, preparation, Visualization, Investigation, Supervision, Software, Validation, Writing – review & editing. Julio Estavillo: Supervision, Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

APPENDIX I. QUESTIONNAIRE (this questionnaire is based on and adapted from those carried out by UNESCO (iesalc) and the Universidad Pública de Navarra) UNESCO iesalc, 2021 , Universidad Pública de Navarra

Confidentiality agreement.

This survey is part of a teaching research project that aims to evaluate the return of students to face-to-face teaching. The survey is anonymous, and the data will only be treated in aggregate form, and your confidentiality will be protected. Thank you very much for your collaboration.

Question 1. Indicate if you are male or female
Question 2. What is your year of birth?
Question 3. How many years have you been at the Universidad Politécnica de Madrid?
Question 4. How many courses are you taking this semester?
• a) affected your performance in your studies during that period.
• b) How they affect you when you return to the classroom.

Scale from 1 to 5, where 1 is very little, 2 is little, 3 is neutral, 4 is quite a lot and 5 is a lot.

• a) before confinement
• b) during confinement
• c) during the return to presence

Please indicate any of the following three options, 1) work, 2) volunteering, 3) care for the elderly or minors.

• a) It has improved during the confinement and this improvement has been maintained during the return to face-to-face
• b) Improved only during confinement
• c) Improved after return to face-to-face confinement
• d) No improvement, it was already adequate
• e) No improvement despite the fact that it was inadequate

Please indicate any the following four options, 1) oral exam, 2) written exam, 3) multiple-choice exam.

Question 10. How has the Covid situation affected your qualifications?

Scale from 1 to 5, where 1) are worse, 2) are somewhat worse, 3) are neutral, 4) are somewhat better, 5) are better.

Question 11. Compared to face-to-face teaching, I consider that with online teaching:

Scale from 1 to 5, where 1) I have learned much less, 2) I have learned less, 3) I have learned the same, 4) I have learned more, 5) I have learned much more.

Question 12. The workload due to online teaching has been:

Scale from 1 to 5, where 1) much lower than face-to-face, 2) lower than face-to-face, 3) similar to face-to-face, 4) higher than face-to-face, 5) much higher than face-to-face.

• Videoconference
• Digital presentations
• Written teaching materials
• Problem cases resolution
• Online resolution (webinars)
• Participation in forums etc.

Scale from 1 to 5, where 1) very bad, 2) bad, 3) neutral, 4) good, 5) very good.

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In the winter of 2019, a new coronavirus, now officially called SARS-CoV-2, emerged in Wuhan, China. The virus made the jump from animals to humans and causes a disease called COVID-19. For some people, often children and young adults, SARS-CoV-2 causes few or no symptoms. For others it can lead to severe lung damage and even death.
Mask Use and Ventilation Improvements to Reduce COVID-19
Discussion. During November 16-December 11, 2020, many K-5 schools in Georgia had resumed in-person instruction, §§§§ necessitating implementation of strategies to prevent SARS-CoV-2 transmission within schools, including mask use and improved ventilation. This study found that before the availability of COVID-19 vaccines, the incidence of COVID-19 was 37% lower in schools that ...
COVID-19 Cases and Transmission in 17 K-12 Schools
School-attributable COVID-19 case rates were compared with rates in the surrounding community. School administration and public health officials provided information on COVID-19 cases within schools. During the study period, widespread community transmission was observed, with 7%-40% of COVID-19 tests having positive results.
Impact of COVID-19 on Life of Students: Case Study in Hong Kong
Abstract. COVID-19 has an impact on the day-to-day life of students, with school closure and detrimental effects on health and well-being that cannot be underestimated. A study collected data reflecting the health and well-being of secondary school students entering a programme entitled "Healthy Life Planning: Assist Students to Acquire and ...
Learning loss due to school closures during the COVID-19 pandemic
Vertical lines show the beginning and end of nationwide school closures in 2020. Schools closed nationally on March 16 and reopened on May 11, after 8 wk of remote learning. Our difference-in-differences design compares learning progress between the two testing dates in 2020 to that in the 3 previous years. Open in viewer.
PDF Key Messages and Actions for COVID-19 Prevention and Control in Schools
3. Be a leader in keeping yourself, your school, family and community healthy. Share what you learn about preventing disease with your family and friends, especially with younger children. Model good practices such as sneezing or coughing into your elbow and washing your hands, especially for younger family members. 4.
The COVID-19 impact on reading achievement growth of Grade 3-5 students
The current study aimed to explore the COVID-19 impact on reading achievement growth by Grade 3-5 students in a large urban school district in the U.S. and whether the impact differed by students' demographic characteristics and instructional modality. Specifically, using administrative data from th …
The Impact of COVID-19 on Education: A Meta-Narrative Review
The descriptive and content analysis yielded two major strands of studies: (1) online education and (2) COVID-19 and education, business, economics, and management. The online education strand focused on the issue of technological anxiety caused by online classes, the feeling of belonging to an academic community, and feedback.
The COVID-19 impact on reading achievement growth of Grade 3-5 students
Reading achievement during COVID-19. Learning loss can be conceptualized as the discrepancy between students' assessed academic knowledge and skills and grade-level curricular expectations due to extended gaps or discontinuities in students' education progress (Pier et al., 2021).This concept has often been discussed with reference to summer slides or setbacks even before COVID-19.
UNICEF Education COVID-19 Case Study
UNICEF Education COVID-19 Case Study . Jordan - Keeping children learning during school closures and ensuring their safe return . 18 March 2020, updated to 15 August 2020 ... to Grade 6 as well as 20,000 vulnerable children in Grades 4 to 6 living in refugee camps and informal, temporary. 2 . settlements. Videos complemented the printed ...
GRADE: Moderna COVID-19 Vaccine
The final GRADE assessment was limited to the Phase II and III randomized control trial data. The Moderna COVID-19 vaccine reduced symptomatic laboratory-confirmed COVID-19 when compared to no COVID-19 vaccination (vaccine efficacy: 94.1%; 95% CI: 89.3%, 96.8%) ( Table 3a ). For hospitalization due to COVID-19, 10 events were documented, 9 in ...
Coronavirus disease (COVID-19): Schools
Further studies are underway on the role of children in transmission in and outside of educational settings. WHO is collaborating with scientists around the world to develop protocols that countries can use to study COVID-19 transmission in educational institutions. ... ensure students who have been in contact with a COVID-19 case stay home for ...
PDF GRADE recommendations
1 Clinical management of COVID-19: web annex. COVID-19 Clinical management: Living guidance, 25 January 2021 . Web annex . GRADE recommendations - additional information . Chapter 10. Management of moderate COVID-19: pneumonia treatment ... randomized trials and cohort studies. Case-series were excluded.
School Performance among Children and Adolescents during COVID-19
Similarly, studies predicted that students during the COVID-19 pandemic may face "learning losses", accompanied with challenges in mental health and well-being [10,50,51,52]. On the other hand, according to our findings, there were also a number of students who benefited from online learning [ 10 , 22 , 27 , 30 , 33 , 34 , 37 , 39 , 40 , 43 ].
The impact of the first wave of COVID-19 on students ...
The results of this study imply that COVID-19 had various effects on the education sector. The results are discussed in connection with the introduction of online education during the COVID-19 ...
Covid-19 infection and vaccination during first trimester and risk of
Introduction. Women infected with covid-19 during pregnancy have a higher risk of pregnancy complications.1 2 3 Based on this evidence, and studies showing that pregnant women are at increased risk of severe disease from covid-19,3 4 the authorities in most countries recommend that pregnant women get vaccinated against covid-19.5 6 7 8 Because pregnant women are not often included in ...
Understanding epidemic data and statistics: A case study of COVID‐19
Today's report (5th April 2020; daily updates in the prepared website) shows that the confirmed cases of COVID‐19 in the United States, Spain, Italy, and Germany are 308850, 126168, 124632, and 96092, respectively. Calculating the total case fatality rate (CFR) of Italy (4th April 2020), about 13.3% of confirmed cases have passed away.
COVID-19 in Primary and Secondary School Settings During the First
Limited U.S. data have been reported regarding COVID-19 in students and school staff members as kindergarten through grade 12 (K-12) schools have reopened. ... A school-related case was defined as a COVID-19 case in a student or staff member who had been on campus for class, work, athletics, or other reasons during the 14 days preceding ...
Teacher pay: everything you need to know about the 2024 pay award
The STRB recommended a pay award of 5.5% and this has been accepted in full by the Education Secretary, reflecting the vital contribution teachers make to children's life chances. The 5.5% award would see pay packets increase by over £2,500 for the average classroom teacher, which would take the median salary for 2024/25 to over £49,000 a year.
Frontiers
3.2 Univariate analysis related to negative nucleic acid transformation on the fifth day, tenth day, and fifteenth day. The results showed that when the fifth-day cumulative conversion rate was used as the dependent variable, the independent variables with P < 0.20 were neutrophil (NE, P = 0.17), Glutamic-pyruvic transaminase (ALT, P = 0.19), Glutamic oxalacetic transaminase (AST, P = 0.07 ...
Clinical Presentation of COVID-19: Case Series and Review of the
5.2. Correlation between Clinical Presentation and Clinical Evolution. According to WHO reports, the overall fatality rate for COVID-19 is estimated at 2.3% [ 47 ], but the fatality rate has varied among studies from 1.4% to 4.3% [ 21, 37 ]. In our case series, the overall mortality rate was 2.5%.
Analyzing the impact of COVID-19 on the grades of university education
Based on a case study with economics students at the Universidad Politécnica de Madrid, the article analyses which factors have determined better and worse performance in students' grades. ... Karadag E. Effect of COVID-19 pandemic on grade inflation in higher education in Turkey. PLoS One. 2021; 16 (8) [PMC free article] [Google Scholar]

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At the Beginning of the Pandemic, We Found Large Disparities in Educational Experiences

In the Summer, Parents Shared Their Preferences for the Fall

By October 2020, Parents of Fully in-Person Learners Reported Improvements. But There’s a Long Way to Go for Remote Learners

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The COVID-19 impact on reading achievement growth of Grade 3-5 students in a U.S. urban school district: variation across student characteristics and instructional modalities

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Theoretical background

The characteristics of online education and its effect on dropout

The COVID-19 pandemic and its effect on education: Inequalities in home learning and colleges’ provision of distance teaching during school closure of the COVID-19 lockdown

Teachers’ approach to their grading strategies and shift to online education during the COVID-19 lockdown

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Covid-19 infection and vaccination during first trimester and risk of congenital anomalies: Nordic registry based study

Introduction

Covid-19 infection and vaccination

Congenital anomalies

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Risk of congenital anomalies according to infection with covid-19

Risk of congenital anomalies according to vaccination against covid-19

Including fetal deaths and late induced abortions

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COVID-19 in Primary and Secondary School Settings During the First Semester of School Reopening — Florida, August–December 2020

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FIGURE . Weekly school-related COVID-19 cases reported among students, as a proportion of overall cases in children aged 5–17 years and in the general population — Florida, August–December 2020*

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ORIGINAL RESEARCH article

1 Introduction

2 Materials and methods

2.2 Inclusion criteria

2.3 Exclusion criteria

2.4 Standard for negative conversion

2.5 Clinical Information collection

2.6 Case matching method