the effect of covid 19 on education research paper

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The Effect of COVID-19 on Education

Jacob hoofman.

a Wayne State University School of Medicine, 540 East Canfield, Detroit, MI 48201, USA

Elizabeth Secord

b Department of Pediatrics, Wayne Pediatrics, School of Medicine, Pediatrics Wayne State University, 400 Mack Avenue, Detroit, MI 48201, USA

COVID-19 has changed education for learners of all ages. Preliminary data project educational losses at many levels and verify the increased anxiety and depression associated with the changes, but there are not yet data on long-term outcomes. Guidance from oversight organizations regarding the safety and efficacy of new delivery modalities for education have been quickly forged. It is no surprise that the socioeconomic gaps and gaps for special learners have widened. The medical profession and other professions that teach by incrementally graduated internships are also severely affected and have had to make drastic changes.

• • Virtual learning has become a norm during COVID-19.
• • Children requiring special learning services, those living in poverty, and those speaking English as a second language have lost more from the pandemic educational changes.
• • For children with attention deficit disorder and no comorbidities, virtual learning has sometimes been advantageous.
• • Math learning scores are more likely to be affected than language arts scores by pandemic changes.
• • School meals, access to friends, and organized activities have also been lost with the closing of in-person school.

The transition to an online education during the coronavirus disease 2019 (COVID-19) pandemic may bring about adverse educational changes and adverse health consequences for children and young adult learners in grade school, middle school, high school, college, and professional schools. The effects may differ by age, maturity, and socioeconomic class. At this time, we have few data on outcomes, but many oversight organizations have tried to establish guidelines, expressed concerns, and extrapolated from previous experiences.

General educational losses and disparities

Many researchers are examining how the new environment affects learners’ mental, physical, and social health to help compensate for any losses incurred by this pandemic and to better prepare for future pandemics. There is a paucity of data at this juncture, but some investigators have extrapolated from earlier school shutdowns owing to hurricanes and other natural disasters. 1

Inclement weather closures are estimated in some studies to lower middle school math grades by 0.013 to 0.039 standard deviations and natural disaster closures by up to 0.10 standard deviation decreases in overall achievement scores. 2 The data from inclement weather closures did show a more significant decrease for children dependent on school meals, but generally the data were not stratified by socioeconomic differences. 3 , 4 Math scores are impacted overall more negatively by school absences than English language scores for all school closures. 4 , 5

The Northwest Evaluation Association is a global nonprofit organization that provides research-based assessments and professional development for educators. A team of researchers at Stanford University evaluated Northwest Evaluation Association test scores for students in 17 states and the District of Columbia in the Fall of 2020 and estimated that the average student had lost one-third of a year to a full year's worth of learning in reading, and about three-quarters of a year to more than 1 year in math since schools closed in March 2020. 5

With school shifted from traditional attendance at a school building to attendance via the Internet, families have come under new stressors. It is increasingly clear that families depended on schools for much more than math and reading. Shelter, food, health care, and social well-being are all part of what children and adolescents, as well as their parents or guardians, depend on schools to provide. 5 , 6

Many families have been impacted negatively by the loss of wages, leading to food insecurity and housing insecurity; some of loss this is a consequence of the need for parents to be at home with young children who cannot attend in-person school. 6 There is evidence that this economic instability is leading to an increase in depression and anxiety. 7 In 1 survey, 34.71% of parents reported behavioral problems in their children that they attributed to the pandemic and virtual schooling. 8

Children have been infected with and affected by coronavirus. In the United States, 93,605 students tested positive for COVID-19, and it was reported that 42% were Hispanic/Latino, 32% were non-Hispanic White, and 17% were non-Hispanic Black, emphasizing a disproportionate effect for children of color. 9 COVID infection itself is not the only issue that affects children’s health during the pandemic. School-based health care and school-based meals are lost when school goes virtual and children of lower socioeconomic class are more severely affected by these losses. Although some districts were able to deliver school meals, school-based health care is a primary source of health care for many children and has left some chronic conditions unchecked during the pandemic. 10

Many families report that the stress of the pandemic has led to a poorer diet in children with an increase in the consumption of sweet and fried foods. 11 , 12 Shelter at home orders and online education have led to fewer exercise opportunities. Research carried out by Ammar and colleagues 12 found that daily sitting had increased from 5 to 8 hours a day and binge eating, snacking, and the number of meals were all significantly increased owing to lockdown conditions and stay-at-home initiatives. There is growing evidence in both animal and human models that diets high in sugar and fat can play a detrimental role in cognition and should be of increased concern in light of the pandemic. 13

The family stress elicited by the COVID-19 shutdown is a particular concern because of compiled evidence that adverse life experiences at an early age are associated with an increased likelihood of mental health issues as an adult. 14 There is early evidence that children ages 6 to 18 years of age experienced a significant increase in their expression of “clinginess, irritability, and fear” during the early pandemic school shutdowns. 15 These emotions associated with anxiety may have a negative impact on the family unit, which was already stressed owing to the pandemic.

Another major concern is the length of isolation many children have had to endure since the pandemic began and what effects it might have on their ability to socialize. The school, for many children, is the agent for forming their social connections as well as where early social development occurs. 16 Noting that academic performance is also declining the pandemic may be creating a snowball effect, setting back children without access to resources from which they may never recover, even into adulthood.

Predictions from data analysis of school absenteeism, summer breaks, and natural disaster occurrences are imperfect for the current situation, but all indications are that we should not expect all children and adolescents to be affected equally. 4 , 5 Although some children and adolescents will likely suffer no long-term consequences, COVID-19 is expected to widen the already existing educational gap from socioeconomic differences, and children with learning differences are expected to suffer more losses than neurotypical children. 4 , 5

Special education and the COVID-19 pandemic

Although COVID-19 has affected all levels of education reception and delivery, children with special needs have been more profoundly impacted. Children in the United States who have special needs have legal protection for appropriate education by the Individuals with Disabilities Education Act and Section 504 of the Rehabilitation Act of 1973. 17 , 18 Collectively, this legislation is meant to allow for appropriate accommodations, services, modifications, and specialized academic instruction to ensure that “every child receives a free appropriate public education . . . in the least restrictive environment.” 17

Children with autism usually have applied behavioral analysis (ABA) as part of their individualized educational plan. ABA therapists for autism use a technique of discrete trial training that shapes and rewards incremental changes toward new behaviors. 19 Discrete trial training involves breaking behaviors into small steps and repetition of rewards for small advances in the steps toward those behaviors. It is an intensive one-on-one therapy that puts a child and therapist in close contact for many hours at a time, often 20 to 40 hours a week. This therapy works best when initiated at a young age in children with autism and is often initiated in the home. 19

Because ABA workers were considered essential workers from the early days of the pandemic, organizations providing this service had the responsibility and the freedom to develop safety protocols for delivery of this necessary service and did so in conjunction with certifying boards. 20

Early in the pandemic, there were interruptions in ABA followed by virtual visits, and finally by in-home therapy with COVID-19 isolation precautions. 21 Although the efficacy of virtual visits for ABA therapy would empirically seem to be inferior, there are few outcomes data available. The balance of safety versus efficacy quite early turned to in-home services with interruptions owing to illness and decreased therapist availability owing to the pandemic. 21 An overarching concern for children with autism is the possible loss of a window of opportunity to intervene early. Families of children and adolescents with autism spectrum disorder report increased stress compared with families of children with other disabilities before the pandemic, and during the pandemic this burden has increased with the added responsibility of monitoring in-home schooling. 20

Early data on virtual schooling children with attention deficit disorder (ADD) and attention deficit with hyperactivity (ADHD) shows that adolescents with ADD/ADHD found the switch to virtual learning more anxiety producing and more challenging than their peers. 22 However, according to a study in Ireland, younger children with ADD/ADHD and no other neurologic or psychiatric diagnoses who were stable on medication tended to report less anxiety with at-home schooling and their parents and caregivers reported improved behavior during the pandemic. 23 An unexpected benefit of shelter in home versus shelter in place may be to identify these stressors in face-to-face school for children with ADD/ADHD. If children with ADD/ADHD had an additional diagnosis of autism or depression, they reported increased anxiety with the school shutdown. 23 , 24

Much of the available literature is anticipatory guidance for in-home schooling of children with disabilities rather than data about schooling during the pandemic. The American Academy of Pediatrics published guidance advising that, because 70% of students with ADHD have other conditions, such as learning differences, oppositional defiant disorder, or depression, they may have very different responses to in home schooling which are a result of the non-ADHD diagnosis, for example, refusal to attempt work for children with oppositional defiant disorder, severe anxiety for those with depression and or anxiety disorders, and anxiety and perseveration for children with autism. 25 Children and families already stressed with learning differences have had substantial challenges during the COVID-19 school closures.

High school, depression, and COVID-19

High schoolers have lost a great deal during this pandemic. What should have been a time of establishing more independence has been hampered by shelter-in-place recommendations. Graduations, proms, athletic events, college visits, and many other social and educational events have been altered or lost and cannot be recaptured.

Adolescents reported higher rates of depression and anxiety associated with the pandemic, and in 1 study 14.4% of teenagers report post-traumatic stress disorder, whereas 40.4% report having depression and anxiety. 26 In another survey adolescent boys reported a significant decrease in life satisfaction from 92% before COVID to 72% during lockdown conditions. For adolescent girls, the decrease in life satisfaction was from 81% before COVID to 62% during the pandemic, with the oldest teenage girls reporting the lowest life satisfaction values during COVID-19 restrictions. 27 During the school shutdown for COVID-19, 21% of boys and 27% of girls reported an increase in family arguments. 26 Combine all of these reports with decreasing access to mental health services owing to pandemic restrictions and it becomes a complicated matter for parents to address their children's mental health needs as well as their educational needs. 28

A study conducted in Norway measured aspects of socialization and mood changes in adolescents during the pandemic. The opportunity for prosocial action was rated on a scale of 1 (not at all) to 6 (very much) based on how well certain phrases applied to them, for example, “I comforted a friend yesterday,” “Yesterday I did my best to care for a friend,” and “Yesterday I sent a message to a friend.” They also ranked mood by rating items on a scale of 1 (not at all) to 5 (very well) as items reflected their mood. 29 They found that adolescents showed an overall decrease in empathic concern and opportunity for prosocial actions, as well as a decrease in mood ratings during the pandemic. 29

A survey of 24,155 residents of Michigan projected an escalation of suicide risk for lesbian, gay, bisexual, transgender youth as well as those youth questioning their sexual orientation (LGBTQ) associated with increased social isolation. There was also a 66% increase in domestic violence for LGBTQ youth during shelter in place. 30 LGBTQ youth are yet another example of those already at increased risk having disproportionate effects of the pandemic.

Increased social media use during COVID-19, along with traditional forms of education moving to digital platforms, has led to the majority of adolescents spending significantly more time in front of screens. Excessive screen time is well-known to be associated with poor sleep, sedentary habits, mental health problems, and physical health issues. 31 With decreased access to physical activity, especially in crowded inner-city areas, and increased dependence on screen time for schooling, it is more difficult to craft easy solutions to the screen time issue.

During these times, it is more important than ever for pediatricians to check in on the mental health of patients with queries about how school is going, how patients are keeping contact with peers, and how are they processing social issues related to violence. Queries to families about the need for assistance with food insecurity, housing insecurity, and access to mental health services are necessary during this time of public emergency.

Medical school and COVID-19

Although medical school is an adult schooling experience, it affects not only the medical profession and our junior colleagues, but, by extrapolation, all education that requires hands-on experience or interning, and has been included for those reasons.

In the new COVID-19 era, medical schools have been forced to make drastic and quick changes to multiple levels of their curriculum to ensure both student and patient safety during the pandemic. Students entering their clinical rotations have had the most drastic alteration to their experience.

COVID-19 has led to some of the same changes high schools and colleges have adopted, specifically, replacement of large in-person lectures with small group activities small group discussion and virtual lectures. 32 The transition to an online format for medical education has been rapid and impacted both students and faculty. 33 , 34 In a survey by Singh and colleagues, 33 of the 192 students reporting 43.9% found online lectures to be poorer than physical classrooms during the pandemic. In another report by Shahrvini and colleagues, 35 of 104 students surveyed, 74.5% students felt disconnected from their medical school and their peers and 43.3% felt that they were unprepared for their clerkships. Although there are no pre-COVID-19 data for comparison, it is expected that the COVID-19 changes will lead to increased insecurity and feelings of poor preparation for clinical work.

Gross anatomy is a well-established tradition within the medical school curriculum and one that is conducted almost entirely in person and in close quarters around a cadaver. Harmon and colleagues 36 surveyed 67 gross anatomy educators and found that 8% were still holding in-person sessions and 34 ± 43% transitioned to using cadaver images and dissecting videos that could be accessed through the Internet.

Many third- and fourth-year medical students have seen periods of cancellation for clinical rotations and supplementation with online learning, telemedicine, or virtual rounds owing to the COVID-19 pandemic. 37 A study from Shahrvini and colleagues 38 found that an unofficial document from Reddit (a widely used social network platform with a subgroup for medical students and residents) reported that 75% of medical schools had canceled clinical activities for third- and fourth-year students for some part of 2020. In another survey by Harries and colleagues, 39 of the 741 students who responded, 93.7% were not involved in clinical rotations with in-person patient contact. The reactions of students varied, with 75.8% admitting to agreeing with the decision, 34.7% feeling guilty, and 27.0% feeling relieved. 39 In the same survey, 74.7% of students felt that their medical education had been disrupted, 84.1% said they felt increased anxiety, and 83.4% would accept the risk of COVID-19 infection if they were able to return to the clinical setting. 39

Since the start of the pandemic, medical schools have had to find new and innovative ways to continue teaching and exposing students to clinical settings. The use of electronic conferencing services has been critical to continuing education. One approach has been to turn to online applications like Google Hangouts, which come at no cost and offer a wide variety of tools to form an integrative learning environment. 32 , 37 , 40 Schools have also adopted a hybrid model of teaching where lectures can be prerecorded then viewed by the student asynchronously on their own time followed by live virtual lectures where faculty can offer question-and-answer sessions related to the material. By offering this new format, students have been given more flexibility in terms of creating a schedule that suits their needs and may decrease stress. 37

Although these changes can be a hurdle to students and faculty, it might prove to be beneficial for the future of medical training in some ways. Telemedicine is a growing field, and the American Medical Association and other programs have endorsed its value. 41 Telemedicine visits can still be used to take a history, conduct a basic visual physical examination, and build rapport, as well as performing other aspects of the clinical examination during a pandemic, and will continue to be useful for patients unable to attend regular visits at remote locations. Learning effectively now how to communicate professionally and carry out telemedicine visits may better prepare students for a future where telemedicine is an expectation and allow students to learn the limitations as well as the advantages of this modality. 41

Pandemic changes have strongly impacted the process of college applications, medical school applications, and residency applications. 32 For US medical residencies, 72% of applicants will, if the pattern from 2016 to 2019 continues, move between states or countries. 42 This level of movement is increasingly dangerous given the spread of COVID-19 and the lack of currently accepted procedures to carry out such a mass migration safely. The same follows for medical schools and universities.

We need to accept and prepare for the fact that medial students as well as other learners who require in-person training may lack some skills when they enter their profession. These skills will have to be acquired during a later phase of training. We may have less skilled entry-level resident physicians and nurses in our hospitals and in other clinical professions as well.

The COVID-19 pandemic has affected and will continue to affect the delivery of knowledge and skills at all levels of education. Although many children and adult learners will likely compensate for this interruption of traditional educational services and adapt to new modalities, some will struggle. The widening of the gap for those whose families cannot absorb the teaching and supervision of education required for in-home education because they lack the time and skills necessary are not addressed currently. The gap for those already at a disadvantage because of socioeconomic class, language, and special needs are most severely affected by the COVID-19 pandemic school closures and will have the hardest time compensating. As pediatricians, it is critical that we continue to check in with our young patients about how they are coping and what assistance we can guide them toward in our communities.

Clinics care points

• • Learners and educators at all levels of education have been affected by COVID-19 restrictions with rapid adaptations to virtual learning platforms.
• • The impact of COVID-19 on learners is not evenly distributed and children of racial minorities, those who live in poverty, those requiring special education, and children who speak English as a second language are more negatively affected by the need for remote learning.
• • Math scores are more impacted than language arts scores by previous school closures and thus far by these shutdowns for COVID-19.
• • Anxiety and depression have increased in children and particularly in adolescents as a result of COVID-19 itself and as a consequence of school changes.
• • Pediatricians should regularly screen for unmet needs in their patients during the pandemic, such as food insecurity with the loss of school meals, an inability to adapt to remote learning and increased computer time, and heightened anxiety and depression as results of school changes.

The authors have nothing to disclose.

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Open Access

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Research Article

Effects of the COVID-19 pandemic in higher education: A data driven analysis for the knowledge acquisition process

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Instituto de Física de Líquidos y Sistemas Biológicos (UNLP-CONICET), La Plata, Argentina, Departamento de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), La Plata, Argentina, Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca, Spain

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

Affiliation Departamento de Física Médica, Centro Atómico Bariloche, CONICET, CNEA, Bariloche, Argentina

Roles Data curation, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

Affiliation Departamento de Estadística, Centro Regional Universitario Bariloche (CRUB) Universidad Nacional del Comahue (UNCOMA), Neuquén, Argentina

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Affiliations División Física Estadística e Interdisciplinaria, Centro Atómico Bariloche and CONICET, Bariloche, Argentina, Profesorado en Física, Universidad Nacional de Río Negro (UNRN), Bariloche, Argentina

• Fátima Velásquez-Rojas, 
• Jesus E. Fajardo, 
• Daniela Zacharías, 
• María Fabiana Laguna

• Published: September 7, 2022
• https://doi.org/10.1371/journal.pone.0274039
• Reader Comments

The COVID-19 pandemic abruptly changed the classroom context and presented enormous challenges for all actors in the educational process, who had to overcome multiple difficulties and incorporate new strategies and tools to construct new knowledge. In this work we analyze how student performance was affected, for a particular case of higher education in La Plata, Argentina. We developed an analytical model for the knowledge acquisition process, based on a series of surveys and information on academic performance in both contexts: face-to-face (before the onset of the pandemic) and virtual (during confinement) with 173 students during 2019 and 2020. The information collected allowed us to construct an adequate representation of the process that takes into account the main contributions common to all individuals. We analyzed the significance of the model by means of Artificial Neural Networks and a Multiple Linear Regression Method. We found that the virtual context produced a decrease in motivation to learn. Moreover, the emerging network of contacts built from the interaction between peers reveals different structures in both contexts. In all cases, interaction with teachers turned out to be of the utmost importance in the process of acquiring knowledge. Our results indicate that this process was also strongly influenced by the availability of resources of each student. This reflects the reality of a developing country, which experienced prolonged isolation, giving way to a particular learning context in which we were able to identify key factors that could guide the design of strategies in similar scenarios.

Citation: Velásquez-Rojas F, Fajardo JE, Zacharías D, Laguna MF (2022) Effects of the COVID-19 pandemic in higher education: A data driven analysis for the knowledge acquisition process. PLoS ONE 17(9): e0274039. https://doi.org/10.1371/journal.pone.0274039

Editor: Jianguo Wang, China University of Mining and Technology, CHINA

Received: September 7, 2021; Accepted: August 19, 2022; Published: September 7, 2022

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

Data Availability: All relevant data are within the paper and its Supporting information files.

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

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

Introduction

The process of acquiring knowledge is one of the most complex for the human being since it involves individual and social processes that have been studied by various epistemological currents [ 1 ]. The educational context where this process is developed is of great relevance since it represents the meeting space between teachers and students, in which a fundamental part of the construction of new knowledge occurs.

The COVID-19 pandemic abruptly changed this context with classroom closures of unprecedented extent and duration, disrupting conventional education in schools and universities around the world. Such measures were an extension of the isolation established in many countries to mitigate the effects of COVID-19, given that social distancing proved to be one of the most effective strategies [ 2 – 10 ].

The educational community as a whole made an enormous effort to quickly adapt to the distance and online learning that this lockdown brought [ 11 ], but it is no less true that students were forced to rely much more on their own resources to sustain the continuity of their learning during this period [ 11 – 13 ]. In the particular case of Argentina the confinement measures began on March 20, 2020, affected all educational levels and coincided with the beginning of the first semester of the academic year.

The new educational context not only brought about great challenges but was also reflected in the results obtained by the students [ 14 – 18 ]. The effects of the change in the learning conditions, although recent and still in process, have been analyzed from different perspectives [ 11 – 17 ]. A less explored methodology, which we propose to address here, is to study this problem from the point of view of complex systems, in line with what was done by some of the authors of this work just before the start of the pandemic [ 19 ]. The reason behind choosing this research design lies in the fact that the approach from this perspective allows the interactions between the individuals involved to be adequately considered when analyzing the effect of a global variable, such as the pandemic. But in addition, the usefulness of mathematical modeling to unravel the relevance of different factors that are present in the knowledge acquisition process was demonstrated in our previous work.

In [ 19 ] we developed an analytical model (the KA model) based on data from a series of surveys that are contrasted with information on academic performance of students, to analyze how the knowledge acquisition depends globally on different extrinsic and intrinsic factors. Regarding the intrinsic factors, one that contributes greatly to the acquisition of knowledge of students is motivation, and this is precisely one of the most affected by the pandemic [ 20 ]. According to the EU report [ 14 ], the closure of physical schools and the adoption of distance education can negatively affect student learning through four main channels: less time spent learning, symptoms of stress, a change in the way that students interact, and lack of motivation to learn. But, it is possible to use a model to assess the hypothesis that lack of motivation is one of the strongest negative impacts of the pandemic on students, regardless of their personal characteristics? In particular, and since the KA model was developed for a specific (face-to-face) context, the first question to be answered in this work should be whether this model is sensitive to modifications of the educational context.

On the other hand, it was already mentioned that the change in physical context affected extrinsic factors that contribute to the acquisition of knowledge, such as the interaction with peers and teachers. This interaction has been found to be essential for the development of positive self-esteem, self-confidence, and a sense of identity. In fact, there is significant evidence showing that social skills are positively associated with cognitive skills and school achievement [ 21 , 22 ]. In this regard, a series of questions arise: From the perspective of the students, did the bond with teachers improve or worsen during the pandemic? Did the interaction between peers change with the change of context? What aspects of it can be measured in the new context?

Analyzing the consequences of the pandemic on the educational performance is a matter of global importance. It is well known that the distance education is essential to ensure the continuity of learning in situations in which face-to-face classes are suspended. In places where virtual and remote strategies were already becoming a reality, the change was a positive [ 18 ]. However, in other countries something as basic as Internet access is still a privilege, guaranteeing distance education cannot be taken for granted. The preparation (or lack thereof) of some countries in this area has revealed the weaknesses of educational methodologies and resources [ 13 ]. Bringing this situation to light is one more step towards fairness.

The previous statements prompt us to seek answers about how much the academic performance of students was affected by the change in the educational context caused by the pandemic. In addition, and in relation to the KA model, we would like to evaluate whether the aspects that we consider relevant have a comparable importance in the construction of knowledge, as well as the consistency of these results when comparing both scenarios.

In this new approach we adapt the analytical model presented in [ 19 ] to compare the knowledge acquisition process in two different contexts: face-to-face (before the onset of the pandemic) and virtual (during the confinement), for a particular case in higher education in Argentina. We present a study that involves 173 students and its entire evolution during 2019 and 2020 in both contexts. Furthermore, and in order to assess the relevance of the parameters we chose for our model, we apply two robust and versatile tools used in multiple applications: Artificial Neural Networks and a Multiple Linear Regression Method.

The article is organized as follows: in the Methods section we describe the participants and its educational context, the data collection and variables (which include the surveys used to construct our data-based model) and the different approaches used to fit the parameters of the model. Then, we present the main results of this work and finally, we summarize and discuss our findings.

Educational context

The research was carried out with several sections of students who attended the Physics II course, corresponding to the second year of Engineering careers at the Faculty of Engineering of the National University of La Plata (UNLP) [ 23 ], Argentina, during the years 2019 and 2020. The Faculty offers 13 engineering degrees, so the interest of the students in the course can vary greatly.

The complete course lasts one semester, with a workload of 8 hours per week divided into 2 theoretical-practical classes. The course consists of two parts, at the end of which a partial written test is taken with a score between 0 and 10. There are two approval regimes: direct promotion, which implies being exempt from the final test (if the average between the two partial exams is 6 or more) or promotion by final exam (if the average is between 4 and 6). Partial tests have an instance of recuperation during the semester and another at the end of it, where the student can improve any of the lower scores obtained in previous tests. This organization was also maintained during the confinement (in virtual context).

Participants

The first part of the research was done during the two semesters of the year 2019, with four different sections in face-to-face context for a total of 81 students (50 male, 31 female). The second part was developed during the year 2020 and also involved four different sections in two semesters, for a total of 92 students (61 male, 31 female). In all cases we had access to the final grade they obtained in the course. In both contexts, we worked with 4 different sections of students for a total of 8 sections, 173 students in 2 years. The initial group of students was much larger, however there were 173 who participated in the whole process. These data are reported in the ( S1 File ) and has been collected with the following actions:

• It does not involve minors.
• It has been collected anonymously. Students have been identified by a numerical code, avoiding gathering of any personal information.
• Students have been informed by the lecturers that some information about their activity could be anonymously collected for statistical purposes. Authors of this study did not receive any objections.
• The tasks related to this study were completely voluntary and they did not in any form alter students’ activities, classes, or the assessment process.

Considering these circumstances, we do not need to apply for ethics approval from our university since no personal data, minors or potentially hazardous activities were involved in the study.

Besides, all teachers involved in the study (some of them also co-authors of this manuscript) who were responsible for the subject taught also gave consent to carry out the study.

We obtained verbal consent from all the participants in the study.

Data collection and variables

We are interested in analyzing and comparing the processes observed in both contexts in terms of the KA model presented in [ 19 ]. A first step consisted in carrying out a classification such as that proposed by Bordogna and Albano [ 24 ] and which proved to be useful in our previous work. This involved separating the students into three different groups according to their final achievements K f , which we relate to the final grade obtained in the course. This was done as follows: (a) High-achieving (HA) students: 8 ≤ K f ≤ 10, (b) Average-achieving (AA) students: 6 < K f < 8 and (c) Low-achieving (LA) students: K f ≤ 6. It is worth noting that students with a final grade lower than 4 are not included in this study.

In Table 1 we show the number of students who participated in the work divided according to their final achievements K f , that we relate to the final grade obtained in the course. Interestingly, and as we found in [ 19 ], the groups have qualitatively different characteristics regarding the relevance of the factors considered in the construction of the new knowledge, as it will be clear shortly.

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https://doi.org/10.1371/journal.pone.0274039.t001

In Fig 1 we display the final grades obtained for all students that we include in the present work. In filled symbols we plot the data in the face-to-face context and the empty symbols represent the data in the virtual context. These data provide us with the information to contrast our theoretical model. A first look at this graph reveals that the marks obtained in the two contexts were different for the HA and LA groups, while the AA group did not present differences. HA students, whose grades were higher than 8, had on average a better performance in virtual context than in face-to-face context. The opposite is seen with the Low-achievement students, LA. To analyze the possible causes of these differences is one of the main purposes of the present paper.

https://doi.org/10.1371/journal.pone.0274039.g001

KA model for both contexts.

It is worth noting that in our study the contribution of peers to the acquisition of knowledge was gathered in two ways: the group conformation and the peer interaction itself. The group conformation includes information on the spatial distribution of the students and the formation of groups, obtained through direct observations of the classes before confinement and through questions in online surveys during confinement. An analysis of the differences in the structure of the peer network formed in each context is carried out in Fig 4 in the Results section.

During the virtual context, important and complementary information was also collected, such as resources the students had (work-space, technological equipment) and the context itself and how it was perceived. Although they are not included as terms in Eq 1 , we carry out a description of the observed situation in the S2 File .

Finally, it should be noted that in our study we focus on a specific type of learning, related to scientific concepts of classical physics. While we are aware that this is not the only value learned in the classroom, we simplify the concept of knowledge to use the final grade as a concrete and quantifiable measure of the student’s performance.

Here we present the surveys carried out on students during each semester of classes ( Table 2 ). The numbers and letters in the last column correspond to the values that we assign to each of them, in order to transfer the answers to the KA model of Eq 1 . The questions marked with (*) were reformulated to adapt them to the virtual context. The surveys carried out in the virtual context were delivered and completed in a digital way using Google tools, while those corresponding to the pre-confinement stage were delivered personally and were completed manually.

Although the surveys were broader, here we only include the questions involved in the model.

https://doi.org/10.1371/journal.pone.0274039.t002

The quantities evaluated more than once (as is the case of M or T ) were averaged in order to have a single value for each factor. Besides, the combination of strategies for the question that measures the interaction with the teacher T in the third survey was given the following numerical values: ABC = AB = AC = BC = 1, A = B = 0.7, C = AD = BD = ABD = ACD = BCD = 0.5, CD = 0.3, D = 0.1 (students could mark several options). These values were given to enhance the use of the strategies provided by the specific section to which the students belonged (options A, B).

From surveys to KA model.

https://doi.org/10.1371/journal.pone.0274039.t003

Proposed tools for analysis

As it was already mentioned, each of these groups has different characteristics regarding the relevance of the factors considered in the construction of Eq 1 . To explicitly measure the weight of each of them we apply two different and complementary approaches: Artificial Neural Networks (ANN) and a Multiple Linear Regression Method (MLR).

In reference [ 25 ], the capability of the ANN for estimating parameters of complex nonlinear and linear problems has been shown. A single-layer perceptron (SLP) constitutes a particular case of the ANN whose output equation resembles Eq 1 . This allows to cross-validate the MLR, which is the most common form of linear regression analysis to treat this kind of problem.

Single Layer Perceptron (SLP) network overview.

To reproduce Eq 1 from an ANN architecture we employed a SLP [ 25 ]. This type of ANN constitutes a particular case of a Multilayer Perceptron (MLP) [ 26 ]. The SLP is a feedforward network of a single artificial neuron-like unit, whose x j inputs (disposed akin to biologic dendrites) are multiplied by a corresponding weight w j and this product is passed to a neuron-like unit where the aforementioned product is added up, as shown in Fig 2 .

The usual ANN notation is in black text and in red text, the equivalent terms corresponding to this particular work are shown. (See Eq 1 ). The element-wise product between the inputs and the weights are added up in the “net input function” stage and suppressing the activation function, an output corresponding to the linear combination of the inputs and the weights is obtained.

https://doi.org/10.1371/journal.pone.0274039.g002

The SLP model was implemented in the programming language Python by means of the Keras package [ 27 ].

Multiple Linear Regression Method.

Besides, β M , β T , β P , β HA , β LA and β F are the regression coefficients corresponding to the variables M , T , P , HA , LA and F , respectively, and they were estimated through the OLS (Ordinary Least Squares) method.

This model was fitted using the function lm() in the programming language R version 4.1.0 [ 28 ].

Comparison between contexts

In our previous work [ 19 ], we compared the results of our KA model with the final grade that the students obtained. Looking for an answer to our main question, about how the educational context affected student performance, we first compare the general results in both, face-to-face and virtual contexts.

We proposed in Eq 1 that the final knowledge reached by a student on a given topic is mainly due to three contributing factors, the personal motivation ( M ), the influence of the teachers ( T ) and the influence of peers ( P ). In Fig 3 we show the average values of the final grade of each group, < K f >, together with average of the data obtained from the surveys carried out, in order to analyze and compare the differences observed with the change of context.

(a) Final grade < K f >, (b) motivation M , (c) interaction with teachers T and (d) interaction between peers P .

https://doi.org/10.1371/journal.pone.0274039.g003

These results allow us to respond positively to our first question, about whether our approach is sensitive to changes in the educational context. As we can see, although the KA model was originally developed for a specific context (face to face), the values of the variables are different for both contexts.

In Fig 3(a) we show the average final grade < K f > for each group of students (HA, AA and LA) and in both contexts. We observe again the differences we first noticed in Fig 1 , related to how the performance of each group is modified with the change of context. For HA students, < K f > increased during the virtual context while for AA and LA it decreased. In what follows, and to deepen the understanding of what is observed, we will analyze what was obtained for the three contributing factors (also averaged for each group), and that we plot in panels (b), (c) and (d) of Fig 3 .

The values of motivation presented in Fig 3(b) reflects a widely studied aspect of the psychological impact of the pandemic on students [ 14 , 20 ]. Our results clearly report the impact of the virtual context on the motivation of students, no matter the group they belong to. This fact should in itself be an alarm to build policies to support the mental health and educational success of the students at all times. If motivation dropped notably in the new virtual context, and the final knowledge is considered as the sum of several factors that contribute to the acquisition of this knowledge, then the way of interacting with peers and teachers also had to change.

The general decrease in the virtual context observed in motivation is not repeated in the other factors analyzed in this study. Fig 3(c) gives us information about the teacher’s contribution from the students’ perspective. Note that for the HA group it has the same weight in both contexts (face-to-face and virtual), while for the AA and LA groups the interaction with teachers increased in the virtual context. Generally, the teacher acts as an intermediary between the activities carried out by the students in order to assimilate the new knowledge and in this new context their presence and support (albeit virtual) was fundamental for many students.

Finally, in Fig 3(d) , we can see the differences in the interaction between peers for each group of students, another issue that was affected during the pandemic.

We can see that HA’s enriched the study in groups in the virtual context in contrast to the other groups of students. We also found that the structure of the emerging contact network from peer interaction presents very different characteristics in both contexts. More details about this aspect of the problem are presented in the next subsection. The situation observed in Fig 3(d) for the interaction between peers is the one that most reflects the behavior of the general performance ( Fig 3(a) ), however the trend is attenuated due to what is observed in Fig 3(b) and 3(c) . These results may partially respond to the change observed in the way students interact.

The aforementioned results can be summarized in Table 4 where we show the relative changes between both contexts. This quantity expresses what it was observed in Fig 3 with the raw data obtained in the surveys: A strong decrease in the motivation term for all groups of students, and different trends in the way of interacting with peers and with teachers depending on the group to which the students belong.

https://doi.org/10.1371/journal.pone.0274039.t004

Networks of peer interactions.

The analysis carried out around Fig 3 indicates that the change in physical context modified the way in which students interact with each other. Furthermore, in this area data was collected in different ways depending on the context. In the face-to-face context, the observations in the classroom were made in situ, with photographic records and paper surveys. During the virtual context, the surveys were digital using Google tools as mentioned above. In the latter case, no observations could be made, so the students were asked how their interaction with the group was and with whom they specifically interacted. This fact could result in a lack of information for this context. However, that was not the case, since although the information collected in both cases is not completely comparable, they suggest a change in behavior in the relationship between peers. Table 5 expresses the number of students who were observed grouped or isolated during the face-to-face classes. Likewise, for the virtual case, the number of students who affirmed to study or not in a group is reported. We find that the percentage of isolated students decreased from 37% to 26% with the change of context. Interestingly, the increase in interaction between students in the virtual context was observed to a greater or lesser extent for the three groups.

https://doi.org/10.1371/journal.pone.0274039.t005

To deepen the understanding of how students modified their way of interacting, we draw in Fig 4(a) the network that represents the students before confinement (face-to-face context) for N = 81. As we said, the data was obtained from direct observations in the classroom, where the nodes represent the students (divided in the HA, AA and LA groups) and the links their interactions. Note that here we use double bonds, indicating a reciprocal interaction.

(a) Network scheme from classroom observations in face-to-face context where the links are reciprocal interactions. (b) Network scheme from data obtained through surveys in virtual context. The links can be or not be reciprocal interactions. The nodes marked with an asterisk represent students who claimed to interact with students from another section who did not participate in this study.

https://doi.org/10.1371/journal.pone.0274039.g004

Besides, in Fig 4(b) we show the network that describes the students in virtual context for N = 92. The data come from the surveys carried out, and again the nodes represent the students divided in the groups HA, AA and LA. We use links to represent their interactions, although now they are double or single, as the responses to the surveys given by the students may or may not be reciprocal. Moreover, the nodes marked with an asterisk represent students who claimed to interact with students from another section who did not participate in this study.

A comparison between both networks indicates some similarities, such as the presence of highly connected clusters, as well as isolated students. However, the network corresponding to the virtual context has nodes that connect two different clusters, acting as “bridges”. This was not observed in the face-to-face context and could mean a new form of relationship between students. This result deepens the understanding of the effect that the pandemic has on peer relationships, and answers some of the questions asked in the introduction on this topic.

Measure of the relevance of the terms that influence the knowledge acquisition process

A way to validate the model presented in Eq 1 is to analyze the relevance of the terms that compose it. In our previous work [ 19 ] we did it by adding coefficients to each factor of the KA model. These coefficients could be interpreted as the relative weight that each term in Eq 1 has, and were chosen so that the average value calculated with the model for each group is as close as possible to the average value of the actual final grades obtained. In order to analyze the relevance and consistency of the factors that we chose to describe the knowledge acquisition process, we now we choose two different and complementary approaches to find the weight of each term of Eq 1 : Artificial Neural Networks (ANN) and a Multiple Linear Regression Method (MLR).

ANN approach.

https://doi.org/10.1371/journal.pone.0274039.g005

https://doi.org/10.1371/journal.pone.0274039.t006

Finally, in Fig 6 we present a comparison between the final grade for each student and the final knowledge obtained from Eq 1 (KA model) with the coefficients obtained with the ANN approach. The global behavior of the KA model follows the general trend of the data. The observed dispersion is due to the presence of particular cases, whose complete evolution is not captured by the model. In our previous work [ 19 ] we made an analysis of some particular cases like these.

https://doi.org/10.1371/journal.pone.0274039.g006

MLR approach.

We make use of the Multiple Linear Regression Method in order to find the weights of each contributing factor of the KA model, and compare them with the ones obtained in the previous section. The results are shown in Table 7 , where we express the values for β , SE (standard error) and p-value for the terms of the Eq 3 .

https://doi.org/10.1371/journal.pone.0274039.t007

The p-values obtained show that all beta regression coefficients are statistically significant. Assumptions of linearity, independence, homoscedasticity and normality were checked, as well as the presence of influential values.

At last, we show in Fig 7 a comparison between the final grade for each student and the final knowledge of Eq 1 (KA model) with the coefficients obtained with the MLR approach. Again, the K f obtained with the model behaves similarly to the data. It should be noted the similarity of the result obtained in Figs 5 and 6 with that shown in Fig 2 of [ 19 ]. In the present work, the adjustment of the weights that gave rise to both figures was carried out in a more appropriate way than in that paper, where the coefficients of each term were chosen exploratory.

https://doi.org/10.1371/journal.pone.0274039.g007

In a previous work we proposed to describe the knowledge acquisition process as a dynamic quantity composed of several terms, where it was implicit that such a process was carried out in the classroom. But, what happens when the physical place where this complex socio-cultural construction takes place changes? What are the consequences of that specific educational context being taken from one day to the next? We seek to answer these questions by discussing how the terms of the knowledge acquisition model were modified, and which ones most directly influenced student performance during the transition to virtuality.

For that, we analyze the knowledge acquisition process in face-to-face and virtual contexts for a specific study case. Our investigation spanned two years and involved 173 students, observing the evolution of their learning process for each particular context.

Inspired by the work of Ref. [ 19 ], we wanted to assess whether the observed changes in academic performance can be understood from a model that incorporates the main factors that contribute to the knowledge acquisition process. The KA model is an analytical model based on data, which incorporates information from a series of surveys and whose results are contrasted with information on academic performance. The surveys were carried out 3 times during each semester and reflected the feelings of the students during their learning process that influenced their performance.

The raw data in Fig 1 show that the final grade of the students in both contexts presented differences. Specifically, the grades of the students with High-achievement (HA) were better in virtual context than in face-to-face context. The opposite is seen with Low-achieving students (LA), while the intermediate performance group (AA) did not show differences. In the results shown in [ 18 ], the performance of the students who used remote learning tools showed an improvement in the virtual context. We believe that this difference is due to the fact that having better resources positioned them in a privileged place with respect to the case studied in this work.

The results obtained in Fig 3 reinforce accepted ideas related to the importance of motivation in the learning process: the switch to virtual context caused a negative impact on the motivation of the entire student population, but was strongly reflected in the performance of LA students. This fact should alert the educational community and especially those responsible for building support mechanisms for the mental health of students. Furthermore, we observe that the new context generates a change in the way students interact with their peers and teachers. In particular, the HA students did not modify the interaction with the teachers (maintaining high values in both contexts) while they strengthened the study in groups in the virtual context, unlike the rest of the groups. For AA and LA students, interaction with teachers increased in the virtual context, and this result highlights the importance of the teacher’s role as a consultant and as fundamental support for students.

We also find that the structure of the network of contacts that is formed between peers in both contexts presents some common characteristics, as well as some interesting differences, as we saw in Fig 4 . Among the first is that both networks have highly connected clusters, as well as a significant number of completely isolated students. The virtual context network, however, shows a feature not observed in the other network: the presence of individuals who interact with one or more students from different clusters. These individuals act as bridges between students who otherwise would not be connected. These structures could be reflecting a new form of relationship between students that occurs more easily in the virtual context. Nevertheless, we are aware that this analysis requires a more detailed investigation that is beyond the scope of this work with the data we currently have. On the other hand, it is also true that the virtual context made it possible to record that the interactions between the students go beyond what happens in the classroom space.

Related with the previous analysis is the fact that, although the equation in the KA model is linear, the term of peers can be interpreted as an effective version of a real non-linear interaction. This term in itself adds complexity to the model since group interaction does not obey “linear” rules. However, the simplification made in the KA model remains valid in light of the results obtained in [ 19 ] and are in line with the idea that the learning process is not limited to the interactive behavior of individual teachers and students, but should be understood in terms of collaborative behavior [ 29 ].

In order to find out the relevance of the factors that we included in the KA model, we used two different approaches: a standard Multiple Linear Regression Method and a Single Layer Perceptron, which is a particular type of Artificial Neural Network.

The results obtained with the neural network ( Fig 5 ) indicate that in both contexts the weights are similar. This result also shows that the raw results adequately describe each context, since the data obtained in each situation reflect the particular reality that each group of students is going through.

Moreover, both approaches indicate a greater relevance of the term of interaction with teachers. We were able to collect information from the teachers to support this fact and the perception of the change in the interaction with the students was also commented on by them (see S2 File ). The knowledge acquisition process comes hand in hand with the importance of the interaction with teachers, and the literalness of their presence in the accompaniment during learning. This result also confirms in some way the universality of the educational act.

The comparisons of Figs 6 and 7 between the raw data and the results obtained with the KA model indicate that the general behavior of individuals can be suitably described with Eq 1 , which is simply the sum of the relative contributions of each of the proposed factors: personal motivation, interaction with peers and influence of teachers. The robustness of the coefficients obtained with the two approaches also indicates that the information collected in the surveys and observations was sufficient to construct an adequate representation of the process. We are aware that this simplification leaves out a huge number of variables that are integrated to give rise to the unique process that each person experiences. But we believe that the results obtained allow us to validate our choice of factors as the main contributions common to all individuals.

Now, we discuss some considerations on the scope and limitations of this work.

One is that we must not lose sight of the fact that the change in the specific physical context brought with it a change in the evaluation criteria. Actually, this aspect was addressed in the teacher interviews that we summarize in the ( S2 File ). As K f is a hard data (the final grade obtained in the course), it would be more appropriate to build new models that consider these data in a more comprehensive way, taking into account the challenges that arose due to the change in this educational context.

Another important issue that is absent from the KA model is the personal context of the students and their available resources. The reason why it was not included is because we had no survey done on these topics in the face-to-face period, so it was not possible to compare both contexts. However, in the Supplementary Material ( S2 File ) we include additional information regarding this subject obtained from the surveys carried out in the virtual context. When asking the students for their feelings regarding confinement, the responses were varied but reluctance was reflected in more than half of the responses. This coincides with our observation about the lack of motivation (see Fig 3(b) ). The emotional stress, widely discussed in this context, goes beyond the academic environment and it was an important characteristic that we tried to capture with our research. Moreover, we found some relevant differences between the students of the different groups, which could influence their performance. Among them, a third of the students belonging to the LA group said they had a poor Internet connection in contrast to the HA group in which this situation occurred for a sixth of the students. More importantly, 13% of students belonging to the LA group did not have a laptop computer and 30% did not have an adequate study space.

These results show how the pandemic has increased educational inequalities at the economic, technological, social and even emotional level of the actors in the educational process. The virtual context promoted a change in teaching and learning methodologies, but it also brought another great challenge that is still far from being resolved, namely access to resources for all students. Hence the importance of recognizing inequalities to make visible the urgent need to build university policies that improve this situation.

A final though has to do with the generalizability of our results. Although this study was done for a specific case, the main factors analyzed here (motivation, interaction with peers and teachers) are not isolated from the global scenario. The generalization of the KA model to other educational scenarios is not only possible but quite straightforward. It should be noted, however, that the part of our study referring to the virtual context was carried out during the first year of the pandemic, so the results obtained could be strongly influenced by the transition between both contexts. Nevertheless, we believe they are valuable in themselves and can serve to deepen the understanding of the complex process of learning.

Supporting information

S1 file. survey data: numerical values associated with the ka model..

https://doi.org/10.1371/journal.pone.0274039.s001

S2 File. Additional information obtained from student and teacher surveys.

https://doi.org/10.1371/journal.pone.0274039.s002

Acknowledgments

The authors acknowledge Dr. José Javier Ramasco for his helpful suggestions on data analysis and availability.

• View Article
• Google Scholar
• PubMed/NCBI
• 9. Sinha D, Tan P. Mathematical model and simulations of COVID-19 2020 outbreak in New York: Predictions and implications for control measures. Available at SSRN 3584911. 2020;.
• 12. Schleicher A. The impact of COVID-19 on education insights from education at a glance 2020. Retrieved from oecd org website: https://www.oecd.org/education/the-impact-of-covid-19-on-education-insights-education-at-a-glance-2020.pdf . 2020;.
• 13. Various. Policy Brief: Education during COVID-19 and beyond. United Nations; 2020.
• 14. Di Pietro G, Biagi F, Costa P, Karpiński Z, Mazza J. The likely impact of COVID-19 on education: Reflections based on the existing literature and recent international datasets. vol. 30275. Publications Office of the European Union; 2020.
• 15. Marinoni G, Van’t Land H, Jensen T. The impact of Covid-19 on higher education around the world. IAU Global Survey Report. 2020;.
• 23. Web page of Engineering Faculty. UNLP; 2021. https://www.ing.unlp.edu.ar/ .
• 26. Goodfellow I, Bengio Y, Courville A. Deep learning. MIT press; 2016.
• 27. Chollet F, et al. Keras; 2015. https://github.com/fchollet/keras .
• 28. Team R. R: A language and environment for statistical computing (R Version 4.1.0, R Foundation for Statistical Computing, Vienna, Austria, 2020); 2021.
• 29. Stamovlasis D, Koopmans M. Complex Dynamical Systems in Education: Concepts, methods and applications; 2016.

SYSTEMATIC REVIEW article

Effects of covid-19-related school closures on student achievement-a systematic review.

• 1 Goethe University Frankfurt, Frankfurt, Germany
• 2 Center for Educational Measurement, Faculty of Educational Sciences, University of Oslo, Oslo, Norway

The COVID-19 pandemic led to numerous governments deciding to close schools for several weeks in spring 2020. Empirical evidence on the impact of COVID-19-related school closures on academic achievement is only just emerging. The present work aimed to provide a first systematic overview of evidence-based studies on general and differential effects of COVID-19-related school closures in spring 2020 on student achievement in primary and secondary education. Results indicate a negative effect of school closures on student achievement, specifically in younger students and students from families with low socioeconomic status. Moreover, certain measures can be identified that might mitigate these negative effects. The findings are discussed in the context of their possible consequences for national educational policies when facing future school closures.

Introduction

In spring 2020, the COVID-19 pandemic caused severe disruption to everyday life around the world. As one of several measures taken to prevent the spread of the virus, many governments closed schools for several weeks or months. Although school closures are considered to be one of the most efficient interventions to curb the spread of the virus ( Haug et al., 2020 ), many educators and researchers raised concerns about the effects of COVID-19-related school closures on student academic achievement and learning inequalities. For instance, Woessmann (2020) estimated a negative effect of 0.10 SD on student achievement due to COVID-19-related school closures. Moreover, Haeck and Lefebvre (2020) estimated that socioeconomic achievement gaps would increase by up to 30%.

The negative effects of school closures due to summer vacation or natural disasters, and of absenteeism on student achievement are already well documented in the literature (for an overview see Kuhfeld et al., 2020a ). Less is known, however, about the impact of COVID-19-related school closures on student achievement. The primary focus of the literature on COVID-19-related school closures to date was on the reception and use of digital learning technologies and remote learning ( Andrew et al., 2020 ; Grewenig et al., 2020 ; Maity et al., 2020 ; Pensiero et al., 2020 ; Blume et al., 2021 ). Moreover, the psychological impact of COVID-19-related school closures, the use of school counseling in connection with COVID-19 ( O'Connor, 2020 ; Xie et al., 2020 ; Ehrler et al., 2021 ; Gadermann et al., 2021 ; O'Sullivan et al., 2021 ), and the effects of the school closures on student motivation ( Zaccoletti et al., 2020 ; Smith et al., 2021 ) were investigated. Existing projections of the impact of COVID-19 on student achievement paint quite a bleak picture. A learning loss of up to 38 points on the Programme for International Student Assessment (PISA 1 ) scale is estimated, which corresponds to an effect size (Cohen's d ) of 0.38 or 0.9 school years ( Azevedo et al., 2020 ; Kuhfeld et al., 2020a ; Wyse et al., 2020 ; Kaffenberger, 2021 ).

Thus, a year into the pandemic, it is a good time for a first stocktaking of the actual, evidence-based impact of COVID-19-related school closures on student achievement. Consequently, the present work aimed to answer two research questions. First, what was the general effect of COVID-19-related school closures in spring 2020 on student achievement in primary and secondary education? Second, did school closures have differential effects on specific student groups?

The review is organized following the reporting guidelines of the PRISMA statement ( Page et al., 2021 ) and structured as follows. We first illustrate our systematic literature search, the inclusion criteria, the risk of bias assessment, and the synthesis of the relevant information from the studies selected. We then report the general and differential effects of the COVID-19-related school closures on student achievement, which are discussed in the context of their possible consequences for future national educational policies.

Literature Search

To identify relevant studies that investigated the effect of COVID-19-related school closures on student achievement, we searched the Web of Science database for articles published between March 1, 2020 and April 30, 2021. We used the following keywords and search string: [Covid OR Corona OR “SARS-CoV-2” AND school AND learn* OR “test score” OR performance OR competenc* OR achievement OR grades]. The results were refined by using the following categories: education, educational research, economics, education scientific disciplines, psychology educational, psychology multidisciplinary, social sciences interdisciplinary, and education special. The indexes searched were SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, and IC. Because the COVID-19 pandemic was still ongoing at the time this review was written, and the field of research on the effects of COVID-19-related school closures on student achievement is rapidly evolving, we additionally searched the preprint servers PsyArXiv, EdArXiv, and SocArXiv using the aforementioned keywords. With this initial literature search, we obtained 601 potentially relevant studies. After selecting relevant articles out of these studies, we used the backward reference searching method (i.e., examining the works cited in the selected articles) to identify additional potentially relevant studies. See Figure 1 for a PRISMA flowchart of the literature search process.

Figure 1 . PRISMA flowchart of the literature search and screening process.

Selection of Studies

The abstracts of the studies selected were carefully read by the authors, and further inclusion was decided based on the following initial criteria. The studies (1) had to have a clear focus on COVID-19-related school closures, they (2) had to focus on primary and secondary education, and they (3) had to have student achievement (or test scores) as the dependent variable. This initial selection left 109 studies for potential inclusion in the review. These studies were thoroughly read by the authors and two research assistants. We carefully assessed the quality of included studies and based the decision to include studies in the review on the following primary set of inclusion criteria: Studies were required (1) to have collected actual data prior to and during/after COVID-19-related school closures, and (2) to have applied statistical analyses and to report an effect size. This set of inclusion criteria was chosen in order to select studies that provided the aforementioned evidence-based insights. Thus, reviews or discussions on how COVID-19 affects educational processes were excluded. Likewise, exploratory analyses or simple surveys (where only percentages were reported) were also excluded. For example, Chadwick and McLoughlin (2021) investigated the impact of COVID-19 related school closures on student's science learning. However, they only questioned teachers on the impact of COVID-19 related school closures on teaching, learning, and assessment. Because the study did not meet the inclusion criteria of having collected actual data on student achievement, including a comparison of data prior to and during/after COVID-19-related school closures, and applying statistical analyses rather than solely reporting percentages, the study was excluded from the systematic review. Similarly, studies by Haeck and Lefebvre (2020) , Kaffenberger (2021) , and Kuhfeld et al. (2020a) were excluded from the systematic review because they reported predicted effects of COVID-19 related school closures on student achievement but did not collect actual data prior to and during/after COVID-19-related school closures.

To determine the degree of rater agreement on the selection of the studies, a randomly selected subset of 20 studies was evaluated by both the authors and the research assistants. Any remaining divergent evaluations were highlighted in the evaluation forms and subsequently discussed. The second selection procedure yielded nine studies that were suitable for inclusion in the review. Subsequently, a backward search of references within the nine selected studies yielded two additional studies, which were then also included in the review.

Risk of Bias Assessment

The Cochrane Risk Assessment of the included studies was conducted independently by the first and second author using the “Risk Of Bias in Non-Randomized Studies of Interventions” tool (ROBINS-I; Sterne et al., 2016 ). The result of the risk assessment is summarized in Figure 2 . Taken together, the highest risk of bias is due to the lack of inclusion of potential confounding variables (Domain 1). Most studies, however, included at least a few relevant controls. Bias due to selection of participants was unlikely as the groups were formed naturally. Similarly, where applicable, interventions were classified correctly. Except for Clark et al. (2020) , no information could be obtained about deviations from intended interventions. This is because the COVID-19 related school closures were not intended interventions. Thus, although there is no information, the risk due to deviations from intended interventions was deemed low. Lastly, bias due to missing data, measurement of outcomes, or selection of reported results is unlikely, as most studies exhibited very small proportions of missing data (except Depping et al., 2021 ), and were highly transparent in their reporting of the results (except, partially, van der Velde et al., 2021 , who only report significant mean differences between groups in sufficient detail). Depping et al. (2021) , however, thoroughly discuss and provide convincing reasons for assuming that missing data does not substantially influence their results.

Figure 2 . Result of the cochrane risk of bias assessment.

We synthesized the eleven studies by extracting the following information that was relevant for our research questions: (1) country, (2) duration of school closure, (3) sample description (type of school and sample size), (4) subjects for which student achievement was investigated, (5) statistical method, (6) general effects of the COVID-19-related school closures on student achievement, and (7) differential effects as reported by subgroup analyses (see Table 1 for a detailed list of the studies included). The focal piece of information was the reported general and differential effects. Where possible, general effects reported in different metrics (e.g., percentile scores), were converted to changes in SD . We then calculated the median of the reported effects, for the overall general effect, as well as for the general effect on reading and mathematics. In light of the relatively small number of studies, random- or even mixed-effects meta-analytic models were not feasible.

Table 1 . Descriptive criteria of studies included.

General Effects of COVID-19-Related School Closures on Student Achievement

The studies on the effect of COVID-19-related school closures on student achievement selected for our review reported mixed findings, with effects ranging from−0.37 SD to +0.25 SD ( Mdn = −0.08 SD ). Most studies found negative effects of COVID-19 related school closures on student achievement. Seven studies reported a negative effect on mathematics ( Clark et al., 2020 ; Kuhfeld et al., 2020b ; Maldonado and De Witte, 2020 ; Tomasik et al., 2020 ; Depping et al., 2021 ; Engzell et al., 2021 ; Schult et al., 2021 ), five studies on reading ( Clark et al., 2020 ; Maldonado and De Witte, 2020 ; Tomasik et al., 2020 ; Engzell et al., 2021 ; Schult et al., 2021 ), and two studies on other subjects, such as science ( Maldonado and De Witte, 2020 ; Engzell et al., 2021 ). This is in line with expected learning losses due to COVID-19 related school closures and the assumption that, in spring 2020, the ad hoc implementation of online teaching gave students, teachers, schools, and parents little time to prepare for or adapt to measures of remote learning.

Three studies reported positive effects of COVID-19 related school closures on student achievement. Meeter (2021) and Spitzer and Musslick (2021) showed students to improve their mathematics achievement when learning with an online-learning software during the COVID-related school closures. Similarly, van der Velde et al. (2021) reported an increase in correct solutions on open questions within a French learning program. Interestingly, these three studies focused on online-learning software. Thus, the positive effects may be explained by the students under investigation being familiar working with the corresponding online-learning software prior to school closures. Hence, they did not have to adapt to a new learning environment when in-person teaching was interrupted due to COVID-19. Moreover, students increased the time using the online-learning software at home, were less distracted or experienced less time pressure in a home-schooling rather than classroom setting, or were presented with individualized assignments within the online program (see also Meeter, 2021 ; Spitzer and Musslick, 2021 ; van der Velde et al., 2021 ).

Additionally, two studies found positive effects on student achievement in mathematics and reading ( Gore et al., 2021 ), or in reading only ( Depping et al., 2021 ). This result might be accounted for by the achievement measurement being timed some months after school closures in both studies and the possibility of effective compensatory measures being implemented by teachers, schools, and local policy makers during this time to counteract learning losses, such as offering learning groups during summer vacation in parts of Germany ( Depping et al., 2021 ).

Even though the median for the effect on mathematics and reading is comparable when averaging above all studies ( d = −0.10 SD and−0.09 SD for mathematics and reading, respectively), some included studies found different effects for different subjects. On the one hand, reasons for finding larger learning losses in reading than in mathematics might be that “mathematics is easier to teach in distance learning, as it is simple to provide exercises and tests digitally or as worksheets” ( Maldonado and De Witte, 2020 , p. 13). As another explanation, many students might not speak the language in which they are tested in at home, hence, not benefitting much in their language skills during school closures (e.g., Maldonado and De Witte, 2020 ). On the other hand, reasons for finding larger learning losses in mathematics than in reading might be that students spent more time on reading during school closures and that supporting children in their reading skills might have been easier to realize for parents than supporting children in improving their competencies in mathematics (e.g., Depping et al., 2021 ; Schult et al., 2021 ).

Differential Effects on Groups of Students

The studies selected for our review reported three main differential effects of COVID-19-related school closures on student achievement in different groups of students. First, the main finding was that younger children were more negatively affected in their learning than older children were (-0.37 SD vs.−0.10 SD ; Tomasik et al., 2020 ). Second, children from families with a low socioeconomic status (SES) were more affected than children from families with a high SES were ( Maldonado and De Witte, 2020 ; Engzell et al., 2021 ). In this context, one study reported an interaction between grade and SES, that is, for younger children from schools with low school-level SES, learning losses of 0.16 SD were found, while younger children from schools with medium school-level SES experienced learning gains of 0.15 SD ( Gore et al., 2021 ). Third, low-performing students were more affected by COVID-19-related school closures in mathematics, while high-performing students were more affected by COVID-19-related school closures in reading ( Schult et al., 2021 ). Finally, low-performing students benefited more from systematic online-learning methods ( Clark et al., 2020 ; Spitzer and Musslick, 2021 ).

As the original studies were not designed to identify the reasons for these effects, additional studies are required to explain the three main differential effects exhaustively. In the following, we provide potential explanations as stated in the original studies. Regarding the first main differential effect (younger students are more affected compared to older students), Tomasik et al. (2020) state that the slower pace of students in primary school may be due to younger children relying more on cognitive scaffolding during instruction, because their capability for self-regulated learning might not be sufficiently developed. From a socio-emotional perspective, younger children might have been more sensitive to stressors related to the COVID-19 pandemic ( Tomasik et al., 2020 ).

The reasons for students from low SES families being more affected relate to access to remote learning, their learning behavior, and the support provided from families and schools. Children from families with a low SES are less likely to have access to remote learning ( UNESCO, 2021 ), are less often provided with active learning assistance from their schools ( Tomasik et al., 2020 ), and spend less time on learning ( Meeter, 2021 ) than children from families with a high SES. Moreover, parents with a high SES are more likely to provide greater psychological support for their children ( OECD, 2019 ), which seems to be specifically relevant in a situation such as the COVID-19 pandemic.

The differential effect on low-performing and high-performing students may be due to high-performing students being capable of improving their performance regardless of the learning environment, while low-performing students specifically benefit from systematic online learning ( Clark et al., 2020 ). Additionally, low-performing students might be less distracted in comparison to learning in a classroom setting ( Spitzer and Musslick, 2021 ). Finally, with the possibility to adapt the assignments in online programs individually to the students, low-performing children might have been addressed more thoroughly according to their needs ( Spitzer and Musslick, 2021 ).

The present work aimed to provide a first systematic overview of studies that reported effects of COVID-19-related school closures on student achievement and to answer two research questions. First, what was the general effect of COVID-19-related school closures in spring 2020 on student achievement in primary and secondary education? Second, did school closures have differential effects on specific student groups?

In sum, there is clear evidence for a negative effect of COVID-19-related school closures on student achievement. The reported effects are comparable in size to findings of research on summer losses ( d = −0.005 SD to −0.05 SD per week; see also Kuhfeld et al., 2020a ) and comparable to Woessmann's initial estimate. Hence, even though remote learning was implemented during COVID-19-related school closures, the effects achieved by remote learning were similar to those achieved when no teaching was implemented at all during summer vacation. Alarmingly, specifically younger children ( Tomasik et al., 2020 ) and children from families with a low SES ( Maldonado and De Witte, 2020 ; Engzell et al., 2021 ) were negatively affected by COVID-19-related school closures. This finding is in line with predictions of widening learning gaps and additive learning losses in subsequent school years ( Grewenig et al., 2020 ; Haeck and Lefebvre, 2020 ; Pensiero et al., 2020 ; Kaffenberger, 2021 ). This indicates that most remote learning measures implemented during the first school closures in spring 2020 were not effective for student learning; there was no difference between them and the absence of systematic teaching during summer vacation.

However, the present review can also identify online-learning measures that seem to be beneficial for student learning. Taking a closer look at studies that reported positive effects of school closures on student achievement, three of these studies ( Meeter, 2021 ; Spitzer and Musslick, 2021 ; van der Velde et al., 2021 ) used some kind of online-learning software to assess student achievement. Students in the studies of both Meeter (2021) and Spitzer and Musslick (2021) worked with online-learning software for mathematics, and students in the study of van der Velde et al. (2021) worked on online-learning software for language learning (i.e., for French). Hence, the positive effects of COVID-19-related school closures on performance in such online-learning programs may have occurred due to the increased use of software during school closures and the fact that students from these studies were familiar working with online-learning programs, hence, did not have to adapt to a new learning environment during COVID-19-related school closures. Additionally, Spitzer and Musslick (2021) reported that low-performing students benefited even more than high-performing students regarding their performance during COVID-19-related school closures from using the learning software. The authors explained this finding by considering that low-performing students were potentially less distracted by other students in a home-learning setting. These findings are in line with results by Clark et al. (2020) , showing low-performing students to specifically benefit from systematic online material.

The present review gives insights into the effects of the COVID-19 related school closures on student achievement in spring 2020. It has to be noted that the number of countries for which evidence of these effects are available is still small, and clustered around developed countries. Especially studies from developing countries are not available yet. We know, however, that the reduction in in-person learning was smaller for low-income countries than for medium-income countries ( UNESCO, 2021 ). Nevertheless, the proportion of students enrolled in primary or secondary education is considerably smaller in poorer countries ( Ward, 2020 ). It may be possible that studies coming from developing countries provide novel insights into the general and especially the differential effects of the COVID-19 related school closures on student achievement. The results of our systematic review can serve as a benchmark for these studies, once they emerge in the literature.

The first COVID-19-related school closures in spring 2020 were followed by similar measures in the fall and winter of 2020/2021. Due to the cumulative nature of learning processes and student achievement, additional learning losses are likely. Nevertheless, school closures do not seem to be initiated as quickly now as they were at the beginning of the pandemic, which is positive for learning. To counter the learning losses, on a micro level, educational policy makers should determine potential supportive measures that increase the active learning time on task. On a macro level, national policy makers should determine potential compensatory measures to support students in their learning and to avoid failed educational careers. In this regard, systematic online material and software have been found to compensate for learning losses, specifically in high-risk children. Hence, educational policy makers and educators should be aware of the importance of providing children with systematic material and ensuring that high-risk children, in particular, have access to adequate learning environments in order to circumvent learning losses and widening learning gaps that may be caused by subsequent school closures. We expect future studies focusing on the subsequent school closures to provide a more differentiated picture of the effects of COVID-19 related school closures on student achievement. For instance, studies may investigate whether there are differences in educational outcomes across countries with differing lockdown measures. Similarly, studies may investigate the reasons for the subject-specific general effects and the three main differential effects identified in this systematic review. Such studies require longitudinal approaches, and may provide educational policy makers with crucial additional information.

The goal of this systematic review was to provide a first evidence-based insight into the effects of COVID-19-related school closures on student achievement in primary and secondary education. The onus is now on national educational policy makers to be aware of these effects and, together with educational and psychological research fields, to work toward the implementation of measures to mitigate or even counteract these negative effects. This may be one of the most important societal tasks for the post-COVID time.

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 author.

Author Contributions

SH and CK conducted the literature review and synthesis, with critical input by AF. SH wrote and revised the manuscript with contributions and feedback provided by CK, TD, and AF. All authors have been involved in the conceptual design of the review.

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.

1. ^ PISA is an international large-scale assessment testing the skills and knowledge of 15-year-old students in three core domains (Mathematics, Reading, and Science). Its main survey is administered every three years, with each rotating as the major domain. The results of the main surveys are scaled to fit approximately normal distributions, with means around 500 score points and standard deviations around 100 score points. A 10-point difference on the PISA scale corresponds to an effect size (Cohen's d ) of 0.10 ( OECD, 2019 ).

Andrew, A., Cattan, S., Costa-Dias, M., Farquharson, C., Kraftman, L., Krutikova, S., et al. (2020). Learning During the Lockdown: REAL-time data on Children's Experiences During Home Learning. The Institute for Fiscal Studies . Available online at: https://www.ifs.org.uk/publications/14848

Google Scholar

Azevedo, J. P., Hasan, A., Goldemberg, D., Iqbal, S. A., and Geven, K. (2020). Simulating the Potential Impacts of COVID-19 School Closures on Schooling and Learning Outcomes: A Set of Global Estimates . Washington, D.C.: The World Bank.

Blume, F., Schmidt, A., Kramer, A. C., Schmiedek, F., and Neubauer, A. B. (2021). Homeschooling during the SARS-CoV-2 pandemic: the role of students' trait self-regulation and task attributes of daily learning tasks for students' daily self-regulation. Zeitschrift für Erziehungswissenschaft 1–25. doi: 10.31234/osf.io/tnrdj

PubMed Abstract | CrossRef Full Text | Google Scholar

Chadwick, R., and McLoughlin, E. (2021). Impact of the covid-19 crisis on learning, teaching and facilitation of practical activities in science upon reopening of Irish schools. Irish Educ. Stud. 40, 197–205. doi: 10.1080/03323315.2021.1915838

CrossRef Full Text | Google Scholar

Clark, A. E., Nong, H., Zhu, H., and Zhu, R. (2020). Compensating for Academic Loss: Online Learning and Student Performance During the COVID-19 Pandemic. HAL . Available online at: https://halshs.archives-ouvertes.fr/halshs-02901505

Depping, D., Lücken, M., Musekamp, F., and Thonke, F. (2021). Kompetenzstände Hamburger Schüler * innen vor und während der Corona-Pandemie [Alternative pupils' competence measurement in Hamburg during the Corona pandemic], “in Schule während der Corona-Pandemie. Neue Ergebnisse und Überblick über ein dynamisches Forschungsfeld , eds D. Fickermann and B. Edelste (Münster: Waxmann), 51–79.

Ehrler, M., Werninger, I., Schnider, B., Eichelberger, D. A., Naef, N., Disselhoff, V., et al. (2021). Impact of the COVID-19 pandemic on children with and without risk for neurodevelopmental impairments. Acta Paediatr. 110, 1281–1288. doi: 10.1111/apa.15775

Engzell, P., Frey, A., and Verhagen, M. D. (2021). Learning loss due to school closures during the COVID-19 pandemic. SocArXiv. doi: 10.31235/osf.io/ve4z7

Gadermann, A. C., Thomson, K. C., Richardson, C. G., Gagné, M., McAuliffe, C., Hirani, S., et al. (2021). Examining the impacts of the COVID-19 pandemic on family mental health in Canada: findings from a national cross-sectional study. BMJ Open 11:e042871. doi: 10.1136/bmjopen-2020-042871

Gore, J., Fray, L., Miller, A., Harris, J., and Taggart, W. (2021). The impact of COVID-19 on student learning in New South Wales primary schools: an empirical study. Austr. Educ. Res. 1–33. doi: 10.1007/s13384-021-00436-w

Grewenig, E., Lergetporer, P., Werner, K., Woessmann, L., and Zierow, L. (2020). COVID-19 and Educational Inequality: How School Closures Affect Low-and High-Achieving Students . EconStor. Available online at: http://hdl.handle.net/10419/227347

Haeck, C., and Lefebvre, P. (2020). Pandemic school closures may increase inequality in test scores. Canad. Pub. Policy 46, S82–S87. doi: 10.3138/cpp.2020-055

Haug, N., Geyrhofer, L., Londei, A., Dervic, E., Desvars-Larrive, A., Loreto, V., et al. (2020). Ranking the effectiveness of worldwide COVID-19 government interventions. Nat. Hum. Behav. 4, 1303–1312. doi: 10.1038/s41562-020-01009-0

Kaffenberger, M. (2021). Modelling the long-run learning impact of the Covid-19 learning shock: actions to (more than) mitigate loss. Int. J. Educ. Dev. 81:102326. doi: 10.1016/j.ijedudev.2020.102326

Kuhfeld, M., Soland, J., Tarasawa, B., Johnson, A., Ruzek, E., and Liu, J. (2020a). Projecting the potential impact of COVID-19 school closures on academic achievement. Educ. Res. 49, 549–565. doi: 10.3102/0013189X20965918

Kuhfeld, M., Tarasawa, B., Johnson, A., Ruzek, E., and Lewis, K. (2020b). Learning during COVID-19: Initial findings on students' reading and math achievement and growth . NWEA. Available online at: https://www.ewa.org/sites/main/files/file-attachments/learning_during_covid-19_brief_nwea_nov2020_final.pdf?1606835922

Maity, S., Sahu, T. N., and Sen, N. (2020). Panoramic view of digital education in COVID-19: a new explored avenue. Rev. Educ. 9, 424–426. doi: 10.1002/rev3.3249

Maldonado, J. E., and De Witte, K. (2020). The effect of school closures on standardised student test outcomes . KU Leuven, Faculty of Economics and Business. Available online at: https://lirias.kuleuven.be/retrieve/588087

Meeter, M. (2021). Primary school mathematics during Covid-19: No evidence of learning gaps in adaptive practicing results. PsyArXiv.

O'Connor, M. (2020). School counselling during COVID-19: an initial examination of school counselling use during a 5-week remote learning period. Past. Care Educ. 1–11. doi: 10.1080/02643944.2020.1855674

OECD (2019). PISA 2018 Results (Volume II): Where All Students Can Succeed . OECD Publishing. doi: 10.1787/b5fd1b8f-en

O'Sullivan, K., Clark, S., McGrane, A., Rock, N., Burke, L., Boyle, N., et al. (2021). A qualitative study of child and adolescent mental health during the COVID-19 pandemic in Ireland. Int. J. Environ. Res. Public Health 18:1062. doi: 10.3390/ijerph18031062

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The prisma 2020 statement: An updated guideline for reporting systematic reviews. BMJ 372:n71. doi: 10.31222/osf.io/v7gm2

Pensiero, N., Kelly, A., and Bokhove, C. (2020). Learning inequalities during the Covid-19 pandemic: How families cope with home-schooling . University of Southampton research report.

Schult, J., Mahler, N., Fauth, B., and Lindner, M. A. (2021). Did students learn less during the COVID-19 pandemic? reading and mathematics competencies before and after the first pandemic wave. PsyArXiv. doi: 10.31234/osf.io/pqtgf

Smith, J., Guimond, F. A., Bergeron, J., St-Amand, J., Fitzpatrick, C., and Gagnon, M. (2021). Changes in students' achievement motivation in the context of the COVID-19 pandemic: a function of extraversion/introversion? Educ. Sci. 11:30. doi: 10.3390/educsci11010030

Spitzer, M., and Musslick, S. (2021). Academic performance of K-12 students in an online-learning environment for mathematics increased during the shutdown of schools in wake of the Covid-19 pandemic. PLoS ONE , 16:e0255629. doi: 10.1371/journal.pone.0255629

Sterne, J. A. C., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., et al. (2016). ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ 355:i4919. doi: 10.1136/bmj.i4919

Tomasik, M. J., Helbling, L. A., and Moser, U. (2020). Educational gains of in-person vs. distance learning in primary and secondary schools: a natural experiment during the COVID-19 pandemic school closures in Switzerland. Int. J. Psychol. 56, 566–576. doi: 10.1002/ijop.12728

UNESCO UNICEF, The World Bank, and OECD. (2021). What's Next? Lessons on Education Recovery . Washington, DC: The World Bank.

van der Velde, M., Sense, F., Spijkers, R., Meeter, M., and van Rijn, H. (2021). Lockdown learning: changes in online study activity and performance of Dutch secondary school students during the COVID-19 pandemic. PsyArXiv. doi: 10.31234/osf.io/fr2v8

Ward, M. (2020). PISA for Development: Out-of-School Assessment, PISA in Focus . OECD Publishing. doi: 10.1787/491fb74a-en

Woessmann, L. (2020). Folgekosten ausbleibenden Lernens: Was wir über die Corona-bedingten Schulschließungen aus der Forschung lernen können [Follow-up costs of an absence of learning: What research can teach us about corona-related school closures]. ifo Schnelldienst 73, 38–44. http://hdl.handle.net/10419/225139

Wyse, A. E., Stickney, E. M., Butz, D., Beckler, A., and Close, C. N. (2020). The potential impact of COVID-19 on student learning and how schools can respond. Educ. Measure. Issue Pract. 39, 60–64. doi: 10.1111/emip.12357

Xie, X., Xue, Q., Zhou, Y., Zhu, K., Liu, Q., Zhang, J., et al. (2020). Mental health status among children in home confinement during the coronavirus disease 2019 outbreak in Hubei Province, China. JAMA Pediatr. 174, 898–900. doi: 10.1001/jamapediatrics.2020.1619

Zaccoletti, S., Camacho, A., Correia, N., Aguiar, C., Mason, L., Alves, R. A., et al. (2020). Parents' perceptions of student academic motivation during the COVID-19 lockdown: A cross-country comparison. Front. Psychol. 11. doi: 10.3389/fpsyg.2020.592670

Keywords: systematic review, COVID-19, school closure, student achievement, learning loss

Citation: Hammerstein S, König C, Dreisörner T and Frey A (2021) Effects of COVID-19-Related School Closures on Student Achievement-A Systematic Review. Front. Psychol. 12:746289. doi: 10.3389/fpsyg.2021.746289

Received: 23 July 2021; Accepted: 20 August 2021; Published: 16 September 2021.

Reviewed by:

Copyright © 2021 Hammerstein, König, Dreisörner and Frey. 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: Svenja Hammerstein, hammerstein@psych.uni-frankfurt.de

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.

Teaching in a pandemic: a comparative evaluation of online vs. face-to-face student outcome gains

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• Published: 17 May 2024
• Volume 3 , article number  54 , ( 2024 )

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• Helen Onyeaka 1 ,
• Paolo Passaretti 1 , 2 &
• Jaimie Miller-Friedmann 3  

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The COVID-19 pandemic forced the education sector to transform significantly in order to support students across the world. Technology played a crucial role in enhancing and adapting traditional learning to digital resources and networks, which are now an essential component of education. However, there is concern about the quality of teaching and its effectiveness in remote teaching due to the lack of real-life feel of more traditional face-to-face education. Our study analysed two separate groups of students enrolled in the same course but provided with either face-to-face or remote teaching. The results show that there is no statistically significant difference in students’ performance or gain, even for laboratory work and resulting reports. However, there was a statistically significant difference in Turnitin scores between these groups, with the remote students having higher levels of plagiarism compared to the traditional face-to-face students. These results support the theory that remote teaching can be a valid alternative, if not a substitute, to face-to-face teaching in the future. The study’s findings are expected to help instructors who are thinking about providing programs through blended learning in the post-pandemic era.

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1 Introduction

The COVID-19 pandemic has drastically changed the world. One of the sectors that has experienced a significant transformation in higher education [ 1 , 2 ]. The emergence of the COVID-19 pandemic and the resulting isolation necessitated a drastic change to digitalization in education worldwide [ 1 ]. The profound effects of the pandemic on students have not yet been fully realized, but short-term ramifications include an inability to socialize or work in groups, no access to campus-based classrooms or laboratories, and a cessation of ‘normal’ university life [ 3 , 4 ]. Whilst a wide array of technological advances has given educators the opportunity to engage in non-traditional classroom techniques, the COVID-19 pandemic required educators to learn online pedagogical methods and to quickly adjust to the ‘new normal’ [ 5 ]. For most people, the COVID-19 pandemic and ensuing lockdowns were unique experiences, however, permanently changed the nature of how we work and learn, by proving that working and learning from home are viable alternatives to attending classes or meetings in person. Even now, as the threat of the pandemic abates, hybrid teaching and learning has become a necessary tool in pedagogical content knowledge [ 4 ]. Online and hybrid pedagogies are not necessarily new concepts, but they are novel in the context of normative university education, and especially with regard to courses that require practical application of conceptual learning, like laboratories [ 6 , 7 ]. During the lockdown, educators were forced to shift their teaching online, often without any training or understanding of how the delivery of content must change [ 5 , 8 , 9 ]. Of particular concern to science educators in higher education was how laboratory techniques could possibly be taught without hands-on learning. These concerns were additional to traditional issues in teaching science, like the possibility of plagiarism, or a student falling behind in classwork. Therefore, it is crucial to understand whether there are significant differences in conceptual gains between students who attend lecture/laboratory courses in a face-to-face (F2F) format versus those who attend virtually. To address this question, we studied the conceptual gains of F2F and remotely connected students enrolled in the same course by comparing their final exam, lab report and Turnitin scores of all students.

2 Literature review

A variety of technologies have enabled us to enhance and adapt traditional learning approaches with computer-based resources [ 10 ]. Educators have been encouraged to develop technology-based learning media capable of meeting school curricula and national standards [ 11 ]. The utilization of the latest available technologies, such as cloud servers, 3D printing, Augmented Reality (AR), Smartboards, Video conferencing, FlipGrid, Hybrid Learning and Adaptive Learning Platforms, in science education has been widely applauded as innovative [ 12 , 13 ]. However, most of the scholarly work on technology use in science education was studied or published prior to the pandemic, and many of these studies fold technology into traditional classroom teaching. The pandemic has created an immediacy for robust studies that prove the efficacy of fully online and hybrid science education.

Whilst some educators have been concerned about plagiarism rates and how online education may affect these rates, Ison [ 14 ] debunked the myth that online institutions and learning methods contributed to the prevalence of plagiarism. The study proved that traditional schools had more extreme cases of plagiarism compared to online institutions. Four years later, Ison published a paper on differences in plagiarism between world cultures, in which he showed that Western European students (including those in the UK) plagiarise far less frequently than their counterparts in India and China [ 15 ]. In this paper, he notes that cultural differences account for some of what would be considered plagiarism in the UK,i.e., ‘significant differences were found to exist between Chinese and Western scholars in their perceived requirement to acknowledge authorship of source documents’ [ 15 ]. Turnitin also needs to be considered for what it actually does, as a program, although most universities now require its use in grading as a shortcut to sorting out plagiarised from original papers. A recent study by Gallant, et al., describes the use of Turnitin in determining the originality of laboratory reports,this study found that most of the text flagged by Turnitin was reworded from the textbook, and could not be described as plagiarism (Gallant et al. 2019). Lab experiments are an intrinsic part of science education, and recreating the lab experience online has proved to be a popular challenge [ 16 , 17 , 18 , 19 , 20 ]. Several distinct factors such as reduction in equipment needs, availability at any time from anywhere, and the opportunity for students to learn at their own pace while exploring difficult or interesting concepts, have been highlighted as benefits of using virtual experiments in education [ 21 ]. These virtual laboratory sessions can be utilized by both undergraduate and postgraduate students as lab sessions follow the same principles and only vary in complexity depending on the level of studies [ 22 , 23 ]. However, opinions on how to use virtual experiments in science and their impact on students’ learning outcomes have largely varied. In a review of recent advances, it was reported that students attain a deeper understanding of science when virtual labs are combined with real hands-on labs [ 20 ] and are shown to increase students' grasp of key laboratory techniques [ 24 ]. For instance, virtual labs have been used to carry out dangerous experiments or experiments impossible in real-life situations [ 25 ].

The responses of students to online labs in published literature reflect negative concerns. In a survey done to assess the experience of students who had participated in an online lab exercise, students lamented over missing out on specific lab activities they had long anticipated they would have participated in person. A major concern raised was the lack of access to their lab instructor during the exercise, although the instructor’s e-mail was readily available to the students [ 26 ]. Scheckler [ 10 ] asserted that the virtual lab experience removes participants from the reality of the physical lab, where specimens can be handled physically. Another negative aspect of online labs is the inherent technical problems such as website failure, access to the internet, use of specific technological tools and applications to access the lab, as well as accuracy and continued existence of hyperlinks associated with online labs glossary [ 10 ].

Overall feedback in published literature from students and attitudes towards virtual education has been positive. Students' reception of virtual laboratories has been positive [ 27 ] and they highlighted instant feedback, flexible access, and test–retest reliability as the major benefits of virtual education [ 28 , 29 , 30 ]. Similarly, students have reported increased engagement with laboratory materials and quizzes and knowledge progression during virtual labs in comparison to prior physical laboratory experiences [ 31 ]. Although virtual education has the potential to revolutionize face-to-face (F2F) learning and teaching in higher institutions [ 12 , 13 , 29 ], it has been criticized for lacking real-life feel that face-to-face education offers. Research has shown that face-to-face education plays a critical role in education, especially in science education [ 32 , 33 ]. It has been hypothesized that with augmented reality, sensorial devices, live videos, and interactive videos, technologies used for virtual education can be improved to have a life-like experience while retaining all the benefits a virtual education offers [ 34 ].

From a technological standpoint, introducing virtual learning technology into the education process demands modifications of the existing protocols present in the traditional learning approach [ 11 , 25 , 35 ]. For instance, to input new learning content, educators must at least understand the underlying technology behind virtual learning technology [ 11 , 25 , 35 ]. This has shown to be particularly challenging as creating realistic virtual models of objects requires cooperation between experts on respective subjects and highly skilled programmers and graphic designers [ 11 , 25 , 35 , 36 ]. To be most effective in bringing a traditional science course online, teachers must be trained in the newest technologies and have the time to create the necessary resources to make their virtual courses as successful as their face-to-face teaching.

While it is reasonable to be excited about all the innovative technologies revolutionizing education, it is essential to carefully consider whether virtual education is genuinely beneficial to students’ learning. Several studies have attempted to evaluate the specific benefits of virtual education, particularly virtual labs [ 36 , 37 , 38 , 39 ]. The effectiveness of student learning in both virtual and face-to-face education (laboratory learning activities) have produced contradictory results [ 36 , 37 ], often due to the lack of a control group. Utilizing control groups to evaluate students’ performance in virtual and face-to-face education has produced contrasting results. For example, an investigation that utilized control groups suggested no significant differences in students’ performance in two learning formats, such as traditional and stimulated lab [ 36 ]. On the other hand, another study with a higher sample size proposed that virtual education significantly improved students’ learning outcome gains [ 39 ]. Other studies have also evaluated the benefits of utilizing virtual education in lecturing, assessment, and quizzes [ 39 , 40 , 41 , 42 ]. The emerging picture suggests there were positive achievements in students’ gains with regard to the classroom, but not laboratory work. Interestingly, these outcome gains were independent of class size and subject, and similar gains were achieved with the use of technology and non-technology-dependent techniques [ 39 , 40 , 41 ].

However, significant disagreement exists among science educators regarding the means and purpose of the laboratory component in science courses [ 36 , 37 ]. This varying opinion has become the single biggest factor in the debate regarding the efficacy of non-traditional learning versus traditional learning [ 16 , 18 ]. In a meta-analysis study of trends in virtual and traditional learning, it was revealed that before 2002, less than 70% of the published studies favored online education, while in studies published after 2003, 84% of the studies favored online education [ 43 ]. When focusing on empirical studies after 2005, there is a similar trend regarding favorability and support for virtual and remote learning. The majority of studies reviewed claimed that students’ outcome gains in virtual education were equal to or greater than achievement in face-to-face education [ 44 ].

Not only has virtual education become more prevalent in recent times, but it has also made it easier to accommodate and manage the increasing numbers of students enrolling in undergraduate and graduate programs [ 2 , 11 ]. In a similar vein, the need to find alternative means of instructing students and assessing students’ performance has increased, as one teacher is insufficient to meet the needs of so many students [ 45 ]. Most significantly, there is also a need to ensure that these alternative means have the same effects and outcomes as attained in the face-to-face (F2F) methods of teaching, learning, and assessment, in light of the recent pandemic.

Our study aims to compare two education models in terms of student learning outcomes. Specifically, we compared groups of students, one receiving the lecturer completely virtual and the other attending the class in a traditional face-to-face (F2F) fashion. The study’s findings are expected to aid instructors who are contemplating providing programs using blended learning in the post-pandemic period.

3.1 Teaching module overview

This study is based on the teaching module named “Food and Microbes” of the School of Chemical Engineering at the University of Birmingham, class 2020/21. This module revolves around the current and existing knowledge of food microbiology, introducing students to the basic concepts of epidemiology and the control of infectious diseases, as well as factors affecting food spoilage, the survival of pathogens and the association of specific microbes with certain foods. A variety of teaching methods are employed in this teaching module. Although most of the course is delivered in lecture format, in practice this includes a mixture of formal teaching, case studies, practical exercises, and a laboratory practical. A feature of the course is the inter-relationship between pure and applied microbiology and its application to industrial processes and the understanding of food safety.

3.2 Participants

The 2020/21 class was composed of 30 postgraduate students. Due to the number of students (80% Chinese, 10% British, and the rest from Africa and America) and the experimental nature of the comparative groups, we consider this to be a case study. While 18 students could attend the lecture in person, the remaining 12 students were enrolled from China, and could not travel to the UK to attend the course due to COVID-19 pandemic restrictions. Therefore, the two groups were named face-to-face (F2F) and Remote, respectively. These circumstances forced the teaching sector to adjust and adapt the teaching approach, as well as students’ learning. Moreover, we considered these conditions ideal to record all possible information and compare the teaching and learning between the two groups. In this case, while F2F students were able to attend the class in person, the Remote group was attending the lecture from China via Zoom streaming. For the scope of this study, there was no further categorization (i.e. nationality, gender, etc.) of the students involved. The lecture was streamed live so that all students were participating at the same time. Moreover, all students had access to the teaching material on a dedicated Canvas page. Canvas is a popular learning management system (LMS) used by many universities and educational institutions around the world. It provides a platform for instructors to manage course materials, assignments, quizzes, discussions, and grades, while also offering students a centralized place to access course content, submit assignments, communicate with instructors and peers, and track their progress. Canvas is known for its user-friendly interface and robust features, making it a widely adopted choice in higher education. During the course, all the students participated in the formative quizzes, lab report and final assessment. All marks were uploaded on the Canvas page, which facilitated the collection of the data used for this study.

3.3 Data collection and analysis

We employed a quantitative analysis approach to evaluate the educational outcomes of online vs. face-to-face teaching methods. Data were collected through formative quizzes, summative lab reports, and final exams, and analyzed using independent sample t-test to compare the mean scores of the two independent groups (F2F and Remote) across different assessments. The choice of t-tests is appropriate for comparing the means of two groups when the data are assumed to be normally distributed.

The data of F2F and Remote students employed in this study were stored on the Canvas page of the course and collected through the tutor account. Data were exported and organised in an Excel spreadsheet. Subsequently, data were analysed with Prism GraphPad 9 software. Multiple comparisons with independent sample t-tests were performed. Levene’s test was performed using the average scores of quizzes, final exams, lab reports, and Turnitin scores for both F2F and Remote groups to assess the equality of variances. The test is used to assess the assumption of equal variances between the two groups, which is a necessary condition for conducting independent sample t-tests. Statistical significance was set at p < 0.05. In our analysis, we focused on comparing average scores, median values, and standard deviations (SD) across different assessments (quizzes, final exams, and lab reports) between face-to-face (F2F) and remote learning groups. A two-way ANOVA was conducted to explore the interaction effects between the type of learning and student performance metrics. This was utilized to examine the interaction effects between the type of learning delivery (F2F vs. Remote) and the students' performance metrics across different assessments. This approach helps to understand if the mode of delivery impacts the outcome variables.

The exam was conducted online by all students due to COVID-19 restrictions. The exam was timed and consisted mostly of open-ended questions to minimize the possibility of copying and pasting from external sources. Furthermore, question pools were utilised and set to randomise questions, ensuring that students do not answer a uniform set of questions. Students were given clear instructions on how to access the exam and how much time they had to complete it. The exam format and questions were reviewed by the instructors to ensure that they were relevant to the course content and could effectively assess student knowledge and understanding. In addition, Turnitin was used to check for instances of plagiarism, improper citation, or unoriginal content in student submissions. This software provides a similarity score indicating the percentage of text in the document that matches existing sources. This helps instructors ensure academic integrity and promote originality in student work. To ensure that the exam is taken by the students themselves and not a proxy, all students were mandated to join an exam Zoom meeting, leave their cameras on, but be muted throughout the duration of the exam.

3.4 Ethical considerations

While no formal approval was required due to the nature of the study, we followed strict protocols to ensure participant privacy and obtained informed consent from all students involved. Data anonymization was rigorously implemented; personal identifiers were replaced with unique codes, and demographic details that could potentially reveal participant identity were carefully obscured. The handling of data was conducted with utmost security—stored on encrypted servers with access strictly limited to authorized personnel, and secure protocols were employed for any data transfer. Informed consent was vital to each of the participant engagement process as they were thoroughly briefed about the study's aims, methods, and their rights, ensuring they understood their participation was entirely voluntary and could be withdrawn at any time.

The students attending the module, “Food and Microbes”, were split into two groups based on the type of enrolment, F2F or Remote. During the course, both groups of students were marked via numerous formative quizzes to test their improvement, and then evaluated via a summative lab report and final exam. Two-way between group ANOVAs were conducted on each of these outcome scores—quiz average, summative laboratory report, and final exam—to determine whether there were differences in learning between online and F2F learning groups. In addition, the Turnitin score that is automatically provided by the application Turnitin was analysed to assess the degree to which online students vs. F2F students rely on plagiarism to complete their assignments. These data are presented in Table  1 :

The lab report and the final exam marks shown in Table  1 are the most important since they are essential to pass the class; students must score at least 50% to pass. For quizzes, F2F students had an average score of 83.52 with an SD of 11.26, while Remote students scored an average of 80.18 with an SD of 11.49. The difference was not statistically significant (p > 0.05). The lab report score average for F2F and Remote students was 61% and 65%, respectively. The average score for F2F students was 61.11 (SD = 16.50), and for Remote students, it was 64.67 (SD = 10.44). The difference in scores was not statistically significant (p > 0.05). The final exam score had similar results, with F2F students scoring an average of 79% and Remote students 75%. F2F students achieved an average score of 79.11 (SD = 10.55), compared to Remote students who scored an average of 74.61 (SD = 13.19), again showing no significant difference (p > 0.05). These scores were not significantly different. A notable finding was the difference in Turnitin scores, with F2F students averaging 26.72 (SD = 8.34) and Remote students 40.67 (SD = 8.36), indicating a significant difference (p < 0.05). F2F students have a much higher variance of the data and a statistically significant difference from online students in their Turnitin score. The Turnitin score calculated for the lab report shows a significant difference between the two groups of students. In particular, students attending virtually seem to have a higher level of plagiarism compared to the F2F. Both groups were also subject to several quizzes at the end of each section of this teaching module. Although F2F seems to have a slightly higher score on average, there is no significant difference between the two groups.

4.1 Correlation analysis

The correlation coefficient ranges from − 1 to 1, with values closer to − 1 indicating a strong negative correlation, values closer to 1 indicating a strong positive correlation, and values close to 0 indicating no correlation. From the correlation matrix, we can see that Lab Mark has a strong positive correlation with Exam Score (r = 0.862) suggesting that students who perform well in lab activities tend to score well in the final exam. This implies a consistency in performance across different types of assessments and a weak negative correlation with Turnitin score (r = − 0.199). This weak negative correlation (r = − 0.199) indicates that higher lab scores are slightly associated with lower plagiarism scores. This could suggest that students who engage more authentically with their lab work may be less likely to plagiarize. Turnitin score has a weak negative correlation with Exam Score (r = − 0.239). The weak negative correlation (r = − 0.239) implies that higher instances of plagiarism do not necessarily correlate with higher exam scores, suggesting that plagiarism may not benefit overall student performance. The results suggest that there is a positive correlation between Lab Mark and Exam Score, meaning that students who performed well in the lab also tended to perform well on the exam. However, there is a weak negative correlation between Lab Mark and Turnitin score, indicating that students who performed better in the lab tended to have lower Turnitin scores. The weak negative correlation between Turnitin score and Exam Score suggests that higher Turnitin scores may not necessarily be associated with better exam performance.

4.2 Levene’s test results

The F2F assessment had a mean score of 83.52 when conducted F2F and 79.11 when conducted remotely. The Remote assessment had a mean score of 80.18 when conducted F2F and 74.61 when conducted remotely. These results suggest that the scores were generally higher when the assessments were conducted face-to-face compared to remotely (Table  2 ).

Levene’s test for equality of variances yielded a statistic of 0.397 with a p-value of 0.552. As the Levene's test statistic is 0.397, the value represents the magnitude of the difference in variances between the two groups. A smaller value indicates a smaller difference in variances, suggesting that the assumption of equal variances may hold. As the p-value is greater than 0.05, we fail to reject the null hypothesis, suggesting no significant difference in variances between the F2F and Remote groups. Therefore, the assumption of equal variances holds, validating the use of parametric tests for further statistical analysis.

5 Discussion

The statistical analysis indicates that there were no significant differences in between the F2F and Remote students’ performances on quizzes, the final exam and final laboratory report scores, except for the plagiarism scores. This outcome suggests that remote learning can be as effective as traditional classroom settings in terms of student academic performance. However, the higher plagiarism scores in the Remote group suggest that academic integrity could be a concern that needs to be addressed more rigorously in remote learning environments. The data suggest that there is no statistically significant difference between face-to-face and virtual delivery methods in terms of final marks in the Food and Microbes course. This indicates that virtual teaching could be employed as an alternative or even as the main approach to teaching. However, further research is needed to investigate the effectiveness of virtual teaching on students' well-being and other factors that may affect learning outcomes.

These findings are similar to the study of [ 36 ] which reported no significant difference between performances in a traditional and stimulated food chemistry lab. However, their study only focused on laboratory classes (a traditional hands-on lab and a simulated lab), whereas our study evaluated formal teaching, quizzes and laboratory practical classes. The only significant difference found between these two groups is in the Turnitin score, which implies that Remote students had copied more. Overall, both groups obtained higher marks on quizzes and the final exam, compared to the final lab report. This provides information about the impact of the examination modality. While quizzes and final exams are characterised by multiple-choice questions, the lab report must be written from the ground up. Students are provided with a standard template and general information about how to structure the report and what key information needs to be included. The lower score in lab reports obtained by both groups suggests that this examination modality can be more difficult for students. This could be due to the fact that students are more experienced in learning concepts and providing answers to specific questions, rather than structuring a scientific report. The latter cannot be easily drafted just by knowing the scientific concepts learned during the course. Instead, to write a report it is also necessary to analyse data, organise them logically and comprehensively, as well as writing all the sections ab initio. The lack of significant differences in academic performance between F2F and Remote groups supports the viability of remote learning as a comparable alternative to traditional classroom settings. The significant difference in Turnitin scores points to the need for enhanced strategies to promote academic integrity in remote learning environments. Institutions may need to implement more rigorous checks and balances or provide more education on academic ethics. The strong correlation between lab and exam performance underscores the importance of hands-on activities, even in a virtual environment. Educators should strive to integrate practical, application-based tasks into the curriculum to improve learning outcomes. The slight negative correlation between lab performance and plagiarism indicates that students who are more engaged with coursework may be less inclined to plagiarize. This suggests a need for personalized learning paths and support to enhance engagement and reduce academic dishonesty.

The Turnitin score associated with the lab report is a plagiarism indicator. Higher scores correspond to a high percentage of plagiarised text. In our analysis of Turnitin scores, we recognize that these metrics represent the degree of text similarity rather than direct evidence of plagiarism. Turnitin’s functionality as a similarity checking tool does not definitively distinguish between instances of properly cited work and plagiarized content. Therefore, while higher Turnitin scores observed in remote students suggest increased similarity, this should not be automatically equated with academic dishonesty. It is essential to consider the context of each similarity instance identified by Turnitin to make informed judgments about academic integrity. It is interesting to note that although students are not supervised while writing the lab reports, there is a statistically significant difference between F2F and Remote. This might be indirectly related to the longer distance between students and the institution. Being far away from the university could impact how students are committed to studying, increasing the tendency to copy rather than write lab reports on their own. We can speculate that this could be due to a weaker personal relationship with the lecturer. F2F have a direct and personal interaction with the lecturer, who they might not want to disappoint with a plagiarised report. On the other hand, Remote students might perceive the lecturer just as a virtual tutor, so they do not feel to create any kind of social connection. However, the differences in the Turnitin score could also be unrelated to the remote study approach and be due to other factors not measurable via this study. For example, students' nationality could have played a significant role in this context. As discussed earlier, the Ison study showed that students in China have a different point of view both to plagiarism itself and to those acts which could be considered plagiarism [ 15 ]. The entire online group consisted of Chinese students, and so the reasons discussed in Ison’s paper for plagiarism may apply to this cohort. In addition, other literature notes that Turnitin, whilst useful, tracks similarity, rather than plagiarism. Laboratory reports, as noted by the Gallant, et al., study, will have similarities due to the fact that certain sections of a laboratory report are essentially the same for all students (e.g., method) [ 46 ]. The influence of cultural and educational backgrounds on Turnitin scores warrants consideration, as highlighted by studies like [ 15 ]. Cultural perspectives towards academic writing and citation practices, particularly evident in diverse student populations, can significantly impact Turnitin similarity scores. For instance, in cultures where collective knowledge is valued, there may be a different approach to citing sources and conceptualizing plagiarism. Educational systems also play a role, with variations in emphasis on originality versus collective learning affecting students' writing practices. Therefore, observed differences in Turnitin scores between face-to-face (F2F) and remote students may stem from deeper cultural and educational influences rather than just physical distance. Instructors should recognize and accommodate these diverse perspectives, providing tailored support to foster academic integrity while respecting cultural differences in writing and citation norms. It is important to highlight that similarity scores are not definitive evidence of plagiarism on their own. Instructors need to review the highlighted similarities in context to determine whether they represent legitimate citations, quotations, or instances of improper copying. Additionally, some types of assignments, such as research papers, may naturally have higher similarity scores due to the inclusion of properly cited external sources. The teaching and learning methods described so far are not equal, however, our results show that they can be considered equivalent. Excluding the differences found for the plagiarism, both groups performed equivalently, demonstrating that it is possible to successfully teach and learn a scientific laboratory course remotely. Our study is limited to a specific subject and number of students. It is therefore necessary to study the effect of remote laboratory learning on a larger group and confirm our findings. Moreover, it would be interesting to compare our study with similar approaches in different subjects spanning across human sciences (i.e., history, philosophy, sociology, psychology, etc.) and more analytical subjects (i.e., mathematics, physics, chemistry, etc.).

Remote teaching is not something completely new. Even before the pandemic, many universities were offering 100% online courses, but the pandemic forced schools and universities to accelerate their intentions and improve the teaching methodologies and technologies related to remote teaching. This approach has already shown numerous advantages compared to traditional teaching, such as the possibility of delivering a lecture to a larger number of students without the need for a larger classroom, giving the possibility to students to learn from the most talented and awarded Professors, as well as studying with other students who are far away, without travel. Moreover, students have more flexibility in the way they learn due to the possibility of re-watching recorded lectures, saving the cost of transport to attend university lectures and labs, as well as a reduction in energy costs for the university infrastructures. One of the key challenges encountered during the remote teaching of this course was maintaining student engagement and active participation in the online learning environment. As highlighted by [ 47 ], student engagement is crucial for academic performance in e-learning settings. To mitigate this challenge, we employed several strategies drawn from the literature and our own experiences. Firstly, we intentionally designed interactive learning activities that fostered active student contribution, as emphasized by Rajabalee et al. [ 48 ]. These activities included online discussions, collaborative projects, and opportunities for peer feedback, which have been shown to positively impact student engagement and overall performance in online courses. Secondly, we focused on enhancing learner satisfaction by providing clear communication, prompt feedback, and easily accessible support resources. Rajabalee and Santally [ 49 ] underscore the importance of learner satisfaction in promoting engagement and performance in online modules. We maintained regular communication through multiple channels, offered timely feedback on assignments and queries, and curated a comprehensive set of online resources for students to refer to at their convenience.

Despite these efforts, we acknowledge that the remote learning experience may have presented additional challenges, such as technical difficulties, feelings of isolation, or distractions in home environments. Continuous monitoring, adaptation, and open communication with students were crucial in identifying and addressing these challenges as they arose. By implementing strategies to foster engagement, active contribution, learner satisfaction, and open communication, we aimed to create an effective and supportive online learning environment.

6 Conclusion

According to our study, there is no statistically significant difference between F2F and Remote students’ final marks at the end of the Food and Microbes course that we reported. Therefore, this is an indication that virtual teaching could be employed as an alternative or even as the main approach to teaching. This case study provided important information about the effectiveness of remote teaching in a postgraduate laboratory course. Although the Turnitin score for the two groups significantly differs, the score may not give a definitive understanding of the two groups of students' approach to plagiarism as the Turnitin tool detects only textual plagiarism. Also, the limited number of students involved limits our conclusions. It is important to extend the study to a larger group of students, testing the effects of the same course and its two delivery methods on learning and student performance. This could help to refine the results and provide more insights to improve the remote teaching strategies currently in place.

The Coronavirus pandemic strongly impacted on education at any level. This gave the chance to reshape the approaches to teach, particularly via remote teaching. Although the latter was already known and widely employed by universities and the private sector, the pandemic forced the education sector to rapidly improve and employ the currently available technologies to guarantee the best teaching quality possible. However, to assess whether remote teaching can be considered a viable and robust alternative to F2F, it is still necessary to further investigate its effectiveness on students’ performances as well as students’ well-being.

7 Future research

While the current study was limited to quantitative analysis due to pandemic-related constraints, future research will aim to incorporate qualitative methods such as interviews, focus groups, and possibly case studies in subsequent studies. This will allow for a more holistic evaluation of the educational approaches by capturing the intricacies of student experiences and perceptions that are not readily quantifiable. We believe that integrating both quantitative and qualitative data will provide a more comprehensive understanding of the effectiveness of different teaching modalities.

Furthermore, future research on diversifying assessment techniques holds the promise of enriching our understanding of student learning and engagement. By incorporating a broader spectrum of assessment methods, including formative assessments, project-based assessments, and peer evaluations, educators can gain deeper insights into the multifaceted nature of student progress. Formative assessments offer real-time feedback, allowing for adjustments in teaching strategies and student learning approaches. Project-based assessments encourage practical application of knowledge, fostering critical thinking and problem-solving skills. Peer evaluations promote collaborative learning and self-reflection, essential components of a comprehensive educational experience. Such research could explore the impact of these diverse assessment methods on student motivation, retention of knowledge, and overall academic success.

In the future we would like to explore additional factors influencing student performance in online and face-to-face settings, such as access to resources, level of support provided, and the nature of assessments, providing valuable insights into optimizing remote teaching strategies. Furthermore, conducting longitudinal studies to assess the long-term effects of remote learning on student outcomes and well-being would contribute significantly to understanding the sustainability and efficacy of remote education. Also, future studies could expand the scope by including multiple cohorts or institutions to increase the sample size and enhance the generalizability of the findings. We also aim to explore avenues for responsibly sharing data while protecting participant privacy and adhering to ethical guidelines. This would enhance the reproducibility of our findings and foster collaborative efforts within the research community.

Data availability

The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.

Bilecen B. Commentary: COVID19 pandemic and higher education: International mobility and students’ social protection. Int Migr. 2020;58(4):263–6.

Article   Google Scholar  

Eder R. The remoteness of remote learning: a policy lesson from COVID19. J Interdiscip Stud Educ. 2020;9(1):168–71.

Kedraka K, Kaltsidis C. Effects of the covid-19 pandemic on university pedagogy: Students' experiences and considerations. Eur J Educ Stud. 2020; 7(8).

Onyema EM, Eucheria NC, Obafemi FA, Sen S, Atonye FG, Sharma A, Alsayed AO. Impact of coronavirus pandemic on education. J Educ Pract. 2020;11(13):108–21.

Google Scholar  

Martin F, Wang C, Jokiaho A, May B, Grübmeyer S. Examining faculty readiness to teach online: a comparison of US and German educators. Eur J Open Dist E-learn. 2019;22(1):53–69.

Karalis T, Raikou N. Teaching at the times of COVID-19: inferences and implications for higher education pedagogy. Int J Acad Res Bus Soc Sci. 2020;10(5):479–93.

Ilieva G, Yankova T, Klisarova-Belcheva S, Ivanova S. Effects of COVID-19 pandemic on university students’ learning. Information. 2021;12(4):163.

Basilaia G, Dgebuadze M, Kantaria M, Chokhonelidze G. Replacing the classic learning form at universities as an immediate response to the COVID-19 virus infection in Georgia. Int J Res Appl Sci Eng Technol. 2020;8(3):101–8.

Saha SM, Pranty SA, Rana MJ, Islam MJ, Hossain ME. Teaching during a pandemic: do university teachers prefer online teaching? Heliyon. 2022;8(1): e08663.

Scheckler RK. Virtual labs: a substitute for traditional labs? Int J Dev Biol. 2003;47(2–3):231–6.

Lewis DI. The pedagogical benefits and pitfalls of virtual tools for teaching and learning laboratory practices in the biological sciences. The Higher Education Academy: STEM. 2014.

Ayega D, Khan A. Students experience on the efficacy of virtual labs in online biology. In: 2020 The 4th International Conference on Education and E-Learning. 2020.

Faulconer E, Griffith J, Wood BL, Acharyya S, Roberts D. A comparison of online and traditional chemistry lecture and lab. Chem Educ Res Pract. 2018;19(1):392–7.

Ison DC. Does the online environment promote plagiarism? A comparative study of dissertations from brick-and-mortar versus online institutions. J Online Learn Teach. 2014;10(2):272.

Ison DC. An empirical analysis of differences in plagiarism among world cultures. J High Educ Policy Manag. 2018;40(4):291–304. https://doi.org/10.1080/1360080X.2018.1479949 .

Aljuhani K, Sonbul M, Althabiti M, Meccawy M. Creating a Virtual Science Lab (VSL): the adoption of virtual labs in Saudi schools. Smart Learn Environ. 2018;5(1):1–13.

Darrah M, Humbert R, Finstein J, Simon M, Hopkins J. Are virtual labs as effective as hands-on labs for undergraduate physics? A comparative study at two major universities. J Sci Educ Technol. 2014;23(6):803–14.

Hurtado-Bermúdez S, Romero-Abrio A. The effects of combining virtual laboratory and advanced technology research laboratory on university students’ conceptual understanding of electron microscopy. Interact Learn Environ. 2020;31:1126–41.

Swan B, Coulombe-Quach X-L, Huang A, Godek J, Becker D, Zhou Y. Meeting the needs of gifted and talented students: case study of a virtual learning lab in a rural middle school. J Adv Acad. 2015;26(4):294–319.

Sypsas A, Paxinou E, Kalles D. Reviewing inquiry-based learning approaches in virtual laboratory environment for science education. Διεθνές Συνέδριο για την Ανοικτή & εξ Αποστάσεως Εκπαίδευση. 2020;10(2Α): 74–89.

Wong W-K, Chen K-P, Chang H-M. A comparison of a virtual lab and a microcomputer-based lab for scientific modelling by college students. J Balt Sci Educ. 2020;19(1):157–73.

Bassindale T, LeSuer R, Smith D. Perceptions of a program approach to virtual laboratory provision for analytical and bioanalytical sciences. J Forensic Sci Educ. 2021;3(1).

Sergeevna VY, Lilia SS, Nikolaevna VS, Suvonovich EY. Main Trends in the Organization of the Postgraduate Research and Education Process in the Context of Digitalization of Higher Education. In: 2022 8th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE) (pp. 1–5). IEEE. 2022.

Paxinou E, Georgiou M, Kakkos V, Kalles D, Galani L. Achieving educational goals in microscopy education by adopting virtual reality labs on top of face-to-face tutorials. Res Sci Technol Educ. 2022;40(3):320–39.

Chen X, Song G, Zhang Y. Virtual and remote laboratory development: a review. Earth Space Eng Sci Constr Opera Challeng Environ. 2010; 3843–3852.

Gilman SL. Do online labs work? An assessment of an online lab on cell division. Am Biol Teach. 2006; 68(9).

Ghazali AR, Zainodin EL, Madhavan I, Gnanasundram LS, Nisar N, Abd Rashid R, Tang WW. Perception of online teaching and learning (T&L) activities among postgraduate students in Faculty of Health Sciences, Universiti Kebangsaan Malaysia (UKM). Front Educ. p. 166. 2022.

Keeney-Kennicutt W, Winkelmann K. What can students learn from virtual labs? Committee Comput. Chemical Edu. 2013.

Lynch T, Ghergulescu I. Review of virtual labs as the emerging technologies for teaching STEM subjects. In: INTED2017 Proc. 11th Int. Technol. Educ. Dev. Conf. 6–8 March Valencia Spain, 2017.

Perez S, Massey-Allard J, Butler D, Ives J, Bonn D, Yee N, Roll I. Identifying productive inquiry in virtual labs using sequence mining. In: International conference on artificial intelligence in education. pp. 287–298. 2017, Springer, Cham.

Jennifer GA, Thomas MG, Vijay Solomon R. Does virtual titration experiment meet students’ expectation? Inside out from Indian context. J Chem Educ. 2022;99(3):1280–6.

Hofstein A, Mamlok-Naaman R. The laboratory in science education: the state of the art. Chem Educ Res Pract. 2007;8(2):105–7.

Satterthwait D. Why are’hands-on’science activities so effective for student learning? Teach Sci. 2010;56(2):7–10.

Wang J, Guo D, Jou M. A study on the effects of model-based inquiry pedagogy on students’ inquiry skills in a virtual physics lab. Comput Hum Behav. 2015;49:658–69.

Baladoh S, Elgamal AF, Abas HA. Virtual lab to develop achievement in electronic circuits for hearing-impaired students. Educ Inf Technol. 2017;22(5):2071–85.

Crandall PG, O’Bryan CA, Killian SA, Beck DE, Jarvis N, Clausen E. A comparison of the degree of student satisfaction using a simulation or a traditional wet lab to teach physical properties of ice. J Food Sci Educ. 2015;14(1):24–9.

Barrett TJ, Stull AT, Hsu TM, Hegarty M. Constrained interactivity for relating multiple representations in science: when virtual is better than real. Comput Educ. 2015;81:69–81.

De La Torre L, Guinaldo M, Heradio R, Dormido S. The ball and beam system: a case study of virtual and remote lab enhancement with moodle. IEEE Trans Industr Inf. 2015;11(4):934–45.

Merchant Z, Goetz ET, Cifuentes L, Keeney-Kennicutt W, Davis TJ. Effectiveness of virtual reality-based instruction on students’ learning outcomes in K-12 and higher education: a meta-analysis. Comput Educ. 2014;70:29–40.

Borst CW, Lipari NG, Woodworth JW. Teacher-guided educational VR: Assessment of live and prerecorded teachers guiding virtual field trips. In: 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). 2018.

Dillon E, Tucker B. Lessons for online learning: charter schools’ successes and mistakes have a lot to teach virtual educators. Educ Next. 2011;11(2):50–8.

Stull J, Varnum SJ, Ducette J, Schiller J. The many faces of formative assessment. Int J Teach Learn Higher Educ. 2011;23(1):30–9.

Shachar M, Neumann Y. Twenty years of research on the academic performance differences between traditional and distance learning: Summative meta-analysis and trend examination. MERLOT J Online Learn Teach. 2010;6(2).

Brinson JR. Learning outcome achievement in non-traditional (virtual and remote) versus traditional (hands-on) laboratories: a review of the empirical research. Comput Educ. 2015;87:218–37.

Sriadhi S, Sitompul H, Restu R, Khaerudin S, Wan Yahaya WA. Virtual-laboratory based learning to improve students’ basic engineering competencies based on their spatial abilities. Comput Appl Eng Educ. 2022;30(6):1857–71.

Gallant TB, Picciotto M, Bozinovic G, Tour E. Plagiarism or not? Investigation of Turnitin ® -detected similarity hits in biology laboratory reports. Biochem Mol Biol Educ. 2019;47:370–9. https://doi.org/10.1002/bmb.21236 .

Rajabalee YB, Santally MI, Rennie F. Modelling students’ performances in activity-based E-learning from a learning analytics perspective: implications and relevance for learning design. Int J Dist Educ Technol (IJDET). 2020;18(4):71–93.

Rajabalee BY, Rennie F, Santally MI. The relationship between quality of student contribution in learning activities and their overall performances in an online course. Eur J Open Dist E-learn. 2018;21(1):16–30.

Rajabalee YB, Santally MI. Learner satisfaction, engagement and performances in an online module: implications for institutional e-learning policy. Educ Inf Technol. 2021;26(3):2623–56.

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Onyeaka, H., Passaretti, P. & Miller-Friedmann, J. Teaching in a pandemic: a comparative evaluation of online vs. face-to-face student outcome gains. Discov Educ 3 , 54 (2024). https://doi.org/10.1007/s44217-024-00140-8

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

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

March 3, 2022

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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• Published: 16 June 2020

COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research

• Debra L. Weiner 1 , 2 ,
• Vivek Balasubramaniam 3 ,
• Shetal I. Shah 4 &
• Joyce R. Javier 5 , 6

on behalf of the Pediatric Policy Council

Pediatric Research volume  88 ,  pages 148–150 ( 2020 ) Cite this article

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The COVID-19 pandemic has resulted in unprecedented research worldwide. The impact on research in progress at the time of the pandemic, the importance and challenges of real-time pandemic research, and the importance of a pediatrician-scientist workforce are all highlighted by this epic pandemic. As we navigate through and beyond this pandemic, which will have a long-lasting impact on our world, including research and the biomedical research enterprise, it is important to recognize and address opportunities and strategies for, and challenges of research and strengthening the pediatrician-scientist workforce.

The first cases of what is now recognized as SARS-CoV-2 infection, termed COVID-19, were reported in Wuhan, China in December 2019 as cases of fatal pneumonia. By February 26, 2020, COVID-19 had been reported on all continents except Antarctica. As of May 4, 2020, 3.53 million cases and 248,169 deaths have been reported from 210 countries. 1

Impact of COVID-19 on ongoing research

The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical research, or redirected research to COVID-19. Most clinical trials, except those testing life-saving therapies, have been paused, and most continuing trials are now closed to new enrollment. Ongoing clinical trials have been modified to enable home administration of treatment and virtual monitoring to minimize participant risk of COVID-19 infection, and to avoid diverting healthcare resources from pandemic response. In addition to short- and long-term patient impact, these research disruptions threaten the careers of physician-scientists, many of whom have had to shift efforts from research to patient care. To protect research in progress, as well as physician-scientist careers and the research workforce, ongoing support is critical. NIH ( https://grants.nih.gov/policy/natural-disasters/corona-virus.htm ), PCORI ( https://www.pcori.org/funding-opportunities/applicant-and-awardee-faqs-related-covid-19 ), and other funders acted swiftly to provide guidance on proposal submission and award management, and implement allowances that enable grant personnel to be paid and time lines to be relaxed. Research institutions have also implemented strategies to mitigate the long-term impact of research disruptions. Support throughout and beyond the pandemic to retain currently well-trained research personnel and research support teams, and to accommodate loss of research assets, including laboratory supplies and study participants, will be required to complete disrupted research and ultimately enable new research.

In the long term, it is likely that the pandemic will force reallocation of research dollars at the expense of research areas funded prior to the pandemic. It will be more important than ever for the pediatric research community to engage in discussion and decisions regarding prioritization of funding goals for dedicated pediatric research and meaningful inclusion of children in studies. The recently released 2020 National Institute of Child Health and Development (NICHD) strategic plan that engaged stakeholders, including scientists and patients, to shape the goals of the Institute, will require modification to best chart a path toward restoring normalcy within pediatric science.

COVID-19 research

This global pandemic once again highlights the importance of research, stable research infrastructure, and funding for public health emergency (PHE)/disaster preparedness, response, and resiliency. The stakes in this worldwide pandemic have never been higher as lives are lost, economies falter, and life has radically changed. Ultimate COVID-19 mitigation and crisis resolution is dependent on high-quality research aligned with top priority societal goals that yields trustworthy data and actionable information. While the highest priority goals are treatment and prevention, biomedical research also provides data critical to manage and restore economic and social welfare.

Scientific and technological knowledge and resources have never been greater and have been leveraged globally to perform COVID-19 research at warp speed. The number of studies related to COVID-19 increases daily, the scope and magnitude of engagement is stunning, and the extent of global collaboration unprecedented. On January 5, 2020, just weeks after the first cases of illness were reported, the genetic sequence, which identified the pathogen as a novel coronavirus, SARS-CoV-2, was released, providing information essential for identifying and developing treatments, vaccines, and diagnostics. As of May 3, 2020 1133 COVID-19 studies, including 148 related to hydroxychloroquine, 13 to remdesivir, 50 to vaccines, and 100 to diagnostic testing, were registered on ClinicalTrials.gov, and 980 different studies on the World Health Organization’s International Clinical Trials Registry Platform (WHO ICTRP), made possible, at least in part, by use of data libraries to inform development of antivirals, immunomodulators, antibody-based biologics, and vaccines. On April 7, 2020, the FDA launched the Coronavirus Treatment Acceleration Program (CTAP) ( https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap ). On April 17, 2020, NIH announced a partnership with industry to expedite vaccine development ( https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options ). As of May 1, 2020, remdesivir (Gilead), granted FDA emergency use authorization, is the only approved therapeutic for COVID-19. 2

The pandemic has intensified research challenges. In a rush for data already thousands of manuscripts, news reports, and blogs have been published, but to date, there is limited scientifically robust data. Some studies do not meet published clinical trial standards, which now include FDA’s COVID-19-specific standards, 3 , 4 , 5 and/or are published without peer review. Misinformation from studies diverts resources from development and testing of more promising therapeutic candidates and has endangered lives. Ibuprofen, initially reported as unsafe for patients with COVID-19, resulted in a shortage of acetaminophen, endangering individuals for whom ibuprofen is contraindicated. Hydroxychloroquine initially reported as potentially effective for treatment of COVID-19 resulted in shortages for patients with autoimmune diseases. Remdesivir, in rigorous trials, showed decrease in duration of COVID-19, with greater effect given early. 6 Given the limited availability and safety data, the use outside clinical trials is currently approved only for severe disease. Vaccines typically take 10–15 years to develop. As of May 3, 2020, of nearly 100 vaccines in development, 8 are in trial. Several vaccines are projected to have emergency approval within 12–18 months, possibly as early as the end of the year, 7 still an eternity for this pandemic, yet too soon for long-term effectiveness and safety data. Antibody testing, necessary for diagnosis, therapeutics, and vaccine testing, has presented some of the greatest research challenges, including validation, timing, availability and prioritization of testing, interpretation of test results, and appropriate patient and societal actions based on results. 8 Relaxing physical distancing without data regarding test validity, duration, and strength of immunity to different strains of COVID-19 could have catastrophic results. Understanding population differences and disparities, which have been further exposed during this pandemic, is critical for response and long-term pandemic recovery. The “Equitable Data Collection and Disclosure on COVID-19 Act” calls for the CDC (Centers for Disease Control and Prevention) and other HHS (United States Department of Health & Human Services) agencies to publicly release racial and demographic information ( https://bass.house.gov/sites/bass.house.gov/files/Equitable%20Data%20Collection%20and%20Dislosure%20on%20COVID19%20Act_FINAL.pdf )

Trusted sources of up-to-date, easily accessible information must be identified (e.g., WHO https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov , CDC https://www.cdc.gov/coronavirus/2019-nCoV/hcp/index.html , and for children AAP (American Academy of Pediatrics) https://www.aappublications.org/cc/covid-19 ) and should comment on quality of data and provide strategies and crisis standards to guide clinical practice.

Long-term, lessons learned from research during this pandemic could benefit the research enterprise worldwide beyond the pandemic and during other PHE/disasters with strategies for balancing multiple novel approaches and high-quality, time-efficient, cost-effective research. This challenge, at least in part, can be met by appropriate study design, collaboration, patient registries, automated data collection, artificial intelligence, data sharing, and ongoing consideration of appropriate regulatory approval processes. In addition, research to develop and evaluate innovative strategies and technologies to improve access to care, management of health and disease, and quality, safety, and cost effectiveness of care could revolutionize healthcare and healthcare systems. During PHE/disasters, crisis standards for research should be considered along with ongoing and just-in-time PHE/disaster training for researchers willing to share information that could be leveraged at time of crisis. A dedicated funded core workforce of PHE/disaster researchers and funded infrastructure should be considered, potentially as a consortium of networks, that includes physician-scientists, basic scientists, social scientists, mental health providers, global health experts, epidemiologists, public health experts, engineers, information technology experts, economists and educators to strategize, consult, review, monitor, interpret studies, guide appropriate clinical use of data, and inform decisions regarding effective use of resources for PHE/disaster research.

Differences between adult and pediatric COVID-19, the need for pediatric research

As reported by the CDC, from February 12 to April 2, 2020, of 149,760 cases of confirmed COVID-19 in the United States, 2572 (1.7%) were children aged <18 years, similar to published rates in China. 9 Severe illness has been rare. Of 749 children for whom hospitalization data is available, 147 (20%) required hospitalization (5.7% of total children), and 15 of 147 required ICU care (2.0%, 0.58% of total). Of the 95 children aged <1 year, 59 (62%) were hospitalized, and 5 (5.3%) required ICU admission. Among children there were three deaths. Despite children being relatively spared by COVID-19, spread of disease by children, and consequences for their health and pediatric healthcare are potentially profound with immediate and long-term impact on all of society.

We have long been aware of the importance and value of pediatric research on children, and society. COVID-19 is no exception and highlights the imperative need for a pediatrician-scientist workforce. Understanding differences in epidemiology, susceptibility, manifestations, and treatment of COVID-19 in children can provide insights into this pathogen, pathogen–host interactions, pathophysiology, and host response for the entire population. Pediatric clinical registries of COVID-infected, COVID-exposed children can provide data and specimens for immediate and long-term research. Of the 1133 COVID-19 studies on ClinicalTrials.gov, 202 include children aged ≤17 years. Sixty-one of the 681 interventional trials include children. With less diagnostic testing and less pediatric research, we not only endanger children, but also adults by not identifying infected children and limiting spread by children.

Pediatric considerations and challenges related to treatment and vaccine research for COVID-19 include appropriate dosing, pediatric formulation, and pediatric specific short- and long-term effectiveness and safety. Typically, initial clinical trials exclude children until safety has been established in adults. But with time of the essence, deferring pediatric research risks the health of children, particularly those with special needs. Considerations specific to pregnant women, fetuses, and neonates must also be addressed. Childhood mental health in this demographic, already struggling with a mental health pandemic prior to COVID-19, is now further challenged by social disruption, food and housing insecurity, loss of loved ones, isolation from friends and family, and exposure to an infodemic of pandemic-related information. Interestingly, at present mental health visits along with all visits to pediatric emergency departments across the United States are dramatically decreased. Understanding factors that mitigate and worsen psychiatric symptoms should be a focus of research, and ideally will result in strategies for prevention and management in the long term, including beyond this pandemic. Social well-being of children must also be studied. Experts note that the pandemic is a perfect storm for child maltreatment given that vulnerable families are now socially isolated, facing unemployment, and stressed, and that children are not under the watch of mandated reporters in schools, daycare, and primary care. 10 Many states have observed a decrease in child abuse reports and an increase in severity of emergency department abuse cases. In the short term and long term, it will be important to study the impact of access to care, missed care, and disrupted education during COVID-19 on physical and cognitive development.

Training and supporting pediatrician-scientists, such as through NIH physician-scientist research training and career development programs ( https://researchtraining.nih.gov/infographics/physician-scientist ) at all stages of career, as well as fostering research for fellows, residents, and medical students willing to dedicate their research career to, or at least understand implications of their research for, PHE/disasters is important for having an ongoing, as well as a just-in-time surge pediatric-focused PHE/disaster workforce. In addition to including pediatric experts in collaborations and consortiums with broader population focus, consideration should be given to pediatric-focused multi-institutional, academic, industry, and/or government consortiums with infrastructure and ongoing funding for virtual training programs, research teams, and multidisciplinary oversight.

The impact of the COVID-19 pandemic on research and research in response to the pandemic once again highlights the importance of research, challenges of research particularly during PHE/disasters, and opportunities and resources for making research more efficient and cost effective. New paradigms and models for research will hopefully emerge from this pandemic. The importance of building sustained PHE/disaster research infrastructure and a research workforce that includes training and funding for pediatrician-scientists and integrates the pediatrician research workforce into high-quality research across demographics, supports the pediatrician-scientist workforce and pipeline, and benefits society.

Johns Hopkins Coronavirus Resource Center. Covid-19 Case Tracker. Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). https://coronavirus.jhu.edu/map.html (2020).

US Food and Drug Administration. Coronavirus (COVID-19) update: FDA issues emergency use authorization for potential COVID-19 treatment. FDA News Release . https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-issues-emergency-use-authorization-potential-covid-19-treatment (2020).

Evans, S. R. Fundamentals of clinical trial design. J. Exp. Stroke Transl. Med. 3 , 19–27 (2010).

Article   Google Scholar  

Antman, E. M. & Bierer, B. E. Standards for clinical research: keeping pace with the technology of the future. Circulation 133 , 823–825 (2016).

Food and Drug Administration. FDA guidance on conduct of clinical trials of medical products during COVID-19 public health emergency. Guidance for Industry, Investigators and Institutional Review Boards . https://www.fda.gov/regulatory-information/search-fda-guidance-documents/fda-guidance-conduct-clinical-trials-medical-products-during-covid-19-public-health-emergency (2020).

National Institutes of Health. NIH clinical trials shows remdesivir accelerates recovery from advanced COVID-19. NIH New Releases . https://www.nih.gov/news-events/news-releases/nih-clinical-trial-shows-remdesivir-accelerates-recovery-advanced-covid-19#.XrIX75ZmQeQ.email (2020).

Radcliffe, S. Here’s exactly where we are with vaccines and treatments for COVID-19. Health News . https://www.healthline.com/health-news/heres-exactly-where-were-at-with-vaccines-and-treatments-for-covid-19 (2020).

Abbasi, J. The promise and peril of antibody testing for COVID-19. JAMA . https://doi.org/10.1001/jama.2020.6170 (2020).

CDC COVID-19 Response Team. Coronavirus disease 2019 in children—United States, February 12–April 2, 2020. Morb. Mortal Wkly Rep . 69 , 422–426 (2020).

Agarwal, N. Opinion: the coronavirus could cause a child abuse epidemic. The New York Times . https://www.nytimes.com/2020/04/07/opinion/coronavirus-child-abuse.html (2020).

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Department of Pediatrics, Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA, USA

Debra L. Weiner

Harvard Medical School, Boston, MA, USA

Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Vivek Balasubramaniam

Department of Pediatrics and Division of Neonatology, Maria Fareri Children’s Hospital at Westchester Medical Center, New York Medical College, Valhalla, NY, USA

Shetal I. Shah

Division of General Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, USA

Joyce R. Javier

Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

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All authors made substantial contributions to conception and design, data acquisition and interpretation, drafting the manuscript, and providing critical revisions. All authors approve this final version of the manuscript.

Pediatric Policy Council

Scott C. Denne, MD, Chair, Pediatric Policy Council; Mona Patel, MD, Representative to the PPC from the Academic Pediatric Association; Jean L. Raphael, MD, MPH, Representative to the PPC from the Academic Pediatric Association; Jonathan Davis, MD, Representative to the PPC from the American Pediatric Society; DeWayne Pursley, MD, MPH, Representative to the PPC from the American Pediatric Society; Tina Cheng, MD, MPH, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Michael Artman, MD, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Shetal Shah, MD, Representative to the PPC from the Society for Pediatric Research; Joyce Javier, MD, MPH, MS, Representative to the PPC from the Society for Pediatric Research.

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Correspondence to Debra L. Weiner .

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Weiner, D.L., Balasubramaniam, V., Shah, S.I. et al. COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research. Pediatr Res 88 , 148–150 (2020). https://doi.org/10.1038/s41390-020-1006-3

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Published : 16 June 2020

Issue Date : August 2020

DOI : https://doi.org/10.1038/s41390-020-1006-3

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The importance of Learning Management Systems (LMS) for self-directed learning called for a rapid shift from learner -centered to fully online learning due to COVID-19. This paper examines the effects of Covid-19 Pandemic on education. The study explores the advantages and disadvantages of online education and pertaining challenges that need to be addressed for its successful implementation. During the study, an online survey was used to gather insights on   students’ experiences and difficulties they encountered during the pandemic. During this survey, a total of 582 participants; the majority of whom were undergraduate students (68%) and were women (68%). While mobile phones were revealed to be the most popular e-learning tool (55%), 43% chose laptops or desktops. Importantly, it is worthy to note that 56% of respondents reported not having access to the internet, likewise 54% spend four to five hours daily on online learning. According to the study, for over a half of the participants, the shift to online classes equally led to a shift in their routines. Additionally, of the participants 10% found it challenging to balance between study time and personal time. Distractions at home and participation in other activities were the main causes of non-participation. The mean scores of 3.19 and 2.98, respectively, in the study underscores the challenges of obtaining online classes and the absence of in-person interactions. We therefore strongly recommend that future studies concentrate on these areas as they have the potential to produce even more precise and significant results.

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Why writing by hand beats typing for thinking and learning

Jonathan Lambert

If you're like many digitally savvy Americans, it has likely been a while since you've spent much time writing by hand.

The laborious process of tracing out our thoughts, letter by letter, on the page is becoming a relic of the past in our screen-dominated world, where text messages and thumb-typed grocery lists have replaced handwritten letters and sticky notes. Electronic keyboards offer obvious efficiency benefits that have undoubtedly boosted our productivity — imagine having to write all your emails longhand.

To keep up, many schools are introducing computers as early as preschool, meaning some kids may learn the basics of typing before writing by hand.

But giving up this slower, more tactile way of expressing ourselves may come at a significant cost, according to a growing body of research that's uncovering the surprising cognitive benefits of taking pen to paper, or even stylus to iPad — for both children and adults.

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In kids, studies show that tracing out ABCs, as opposed to typing them, leads to better and longer-lasting recognition and understanding of letters. Writing by hand also improves memory and recall of words, laying down the foundations of literacy and learning. In adults, taking notes by hand during a lecture, instead of typing, can lead to better conceptual understanding of material.

"There's actually some very important things going on during the embodied experience of writing by hand," says Ramesh Balasubramaniam , a neuroscientist at the University of California, Merced. "It has important cognitive benefits."

While those benefits have long been recognized by some (for instance, many authors, including Jennifer Egan and Neil Gaiman , draft their stories by hand to stoke creativity), scientists have only recently started investigating why writing by hand has these effects.

A slew of recent brain imaging research suggests handwriting's power stems from the relative complexity of the process and how it forces different brain systems to work together to reproduce the shapes of letters in our heads onto the page.

Your brain on handwriting

Both handwriting and typing involve moving our hands and fingers to create words on a page. But handwriting, it turns out, requires a lot more fine-tuned coordination between the motor and visual systems. This seems to more deeply engage the brain in ways that support learning.

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"Handwriting is probably among the most complex motor skills that the brain is capable of," says Marieke Longcamp , a cognitive neuroscientist at Aix-Marseille Université.

Gripping a pen nimbly enough to write is a complicated task, as it requires your brain to continuously monitor the pressure that each finger exerts on the pen. Then, your motor system has to delicately modify that pressure to re-create each letter of the words in your head on the page.

"Your fingers have to each do something different to produce a recognizable letter," says Sophia Vinci-Booher , an educational neuroscientist at Vanderbilt University. Adding to the complexity, your visual system must continuously process that letter as it's formed. With each stroke, your brain compares the unfolding script with mental models of the letters and words, making adjustments to fingers in real time to create the letters' shapes, says Vinci-Booher.

That's not true for typing.

To type "tap" your fingers don't have to trace out the form of the letters — they just make three relatively simple and uniform movements. In comparison, it takes a lot more brainpower, as well as cross-talk between brain areas, to write than type.

Recent brain imaging studies bolster this idea. A study published in January found that when students write by hand, brain areas involved in motor and visual information processing " sync up " with areas crucial to memory formation, firing at frequencies associated with learning.

"We don't see that [synchronized activity] in typewriting at all," says Audrey van der Meer , a psychologist and study co-author at the Norwegian University of Science and Technology. She suggests that writing by hand is a neurobiologically richer process and that this richness may confer some cognitive benefits.

Other experts agree. "There seems to be something fundamental about engaging your body to produce these shapes," says Robert Wiley , a cognitive psychologist at the University of North Carolina, Greensboro. "It lets you make associations between your body and what you're seeing and hearing," he says, which might give the mind more footholds for accessing a given concept or idea.

Those extra footholds are especially important for learning in kids, but they may give adults a leg up too. Wiley and others worry that ditching handwriting for typing could have serious consequences for how we all learn and think.

What might be lost as handwriting wanes

The clearest consequence of screens and keyboards replacing pen and paper might be on kids' ability to learn the building blocks of literacy — letters.

"Letter recognition in early childhood is actually one of the best predictors of later reading and math attainment," says Vinci-Booher. Her work suggests the process of learning to write letters by hand is crucial for learning to read them.

"When kids write letters, they're just messy," she says. As kids practice writing "A," each iteration is different, and that variability helps solidify their conceptual understanding of the letter.

Research suggests kids learn to recognize letters better when seeing variable handwritten examples, compared with uniform typed examples.

This helps develop areas of the brain used during reading in older children and adults, Vinci-Booher found.

"This could be one of the ways that early experiences actually translate to long-term life outcomes," she says. "These visually demanding, fine motor actions bake in neural communication patterns that are really important for learning later on."

Ditching handwriting instruction could mean that those skills don't get developed as well, which could impair kids' ability to learn down the road.

"If young children are not receiving any handwriting training, which is very good brain stimulation, then their brains simply won't reach their full potential," says van der Meer. "It's scary to think of the potential consequences."

Many states are trying to avoid these risks by mandating cursive instruction. This year, California started requiring elementary school students to learn cursive , and similar bills are moving through state legislatures in several states, including Indiana, Kentucky, South Carolina and Wisconsin. (So far, evidence suggests that it's the writing by hand that matters, not whether it's print or cursive.)

Slowing down and processing information

For adults, one of the main benefits of writing by hand is that it simply forces us to slow down.

During a meeting or lecture, it's possible to type what you're hearing verbatim. But often, "you're not actually processing that information — you're just typing in the blind," says van der Meer. "If you take notes by hand, you can't write everything down," she says.

The relative slowness of the medium forces you to process the information, writing key words or phrases and using drawing or arrows to work through ideas, she says. "You make the information your own," she says, which helps it stick in the brain.

Such connections and integration are still possible when typing, but they need to be made more intentionally. And sometimes, efficiency wins out. "When you're writing a long essay, it's obviously much more practical to use a keyboard," says van der Meer.

Still, given our long history of using our hands to mark meaning in the world, some scientists worry about the more diffuse consequences of offloading our thinking to computers.

"We're foisting a lot of our knowledge, extending our cognition, to other devices, so it's only natural that we've started using these other agents to do our writing for us," says Balasubramaniam.

It's possible that this might free up our minds to do other kinds of hard thinking, he says. Or we might be sacrificing a fundamental process that's crucial for the kinds of immersive cognitive experiences that enable us to learn and think at our full potential.

Balasubramaniam stresses, however, that we don't have to ditch digital tools to harness the power of handwriting. So far, research suggests that scribbling with a stylus on a screen activates the same brain pathways as etching ink on paper. It's the movement that counts, he says, not its final form.

Jonathan Lambert is a Washington, D.C.-based freelance journalist who covers science, health and policy.

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