Challenges for Education during the Pandemic: An Overview of Literature

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Challenges for Education during the Pandemic: An Overview of Literature

  • Nadezhda Radina,
  • Yulia Balakina


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This article overviews studies exploring the COVID‑19 pandemic’s impact on education systems and their responses to lockdown restrictions, comparing available findings with international statistics based on continuous education system monitoring. Global organizations acknowledge disruption of classical educational processes and emergency transition to distance learning during the pandemic. Scientific literature examines accessibility of online education, alternative forms of distance learning, and the pandemic-induced financial constraints on universities inhibiting new construction, social support for students, scholarship application, professional development of faculty members, and research growth. The pandemic illuminated the issue of inequality in education, which worsened as a result of emergency transition to online studies. In particular, researchers focus on the most vulnerable groups of students, such as children from low-income families, children from migrant backgrounds, and students with disabilities. Projects aimed at studying the digitalization of education account for the biggest chunk of research inspired by the new pandemic reality. A number of studies discuss not just a formal transition to distance learning but a major technological turn that allows using the unique opportunities provided by digital technologies, which is especially important when teaching medical students. Theoretical inquiry is a distinctive feature of scientific discourse, as compared to the discourse of international expert and analytical reports on the problems of education in the context of the COVID‑19 pandemic. Research on changes to the learning process makes it possible to reconstruct the direct and indirect, as well as latent, threats of the pandemic.

  • online education
  • distance learning
  • medical education
  • inequality in education

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  • Published: 30 January 2023

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

  • Bastian A. Betthäuser   ORCID: 1 , 2 , 3 ,
  • Anders M. Bach-Mortensen   ORCID: 2 &
  • Per Engzell   ORCID: 3 , 4 , 5  

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

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

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

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

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

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

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

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

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

The state of the evidence

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

figure 1

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

The geographic reach of evidence is limited

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

The quality of evidence is mixed

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

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

figure 2

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

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

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

No evidence of publication bias

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

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

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

Learning progress slowed substantially during the pandemic

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

figure 3

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

Learning deficits arose early in the pandemic and persist

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

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

figure 4

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

Socio-economic inequality in education increased

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

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

figure 5

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

Learning deficits are larger in maths than in reading

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

figure 6

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

No evidence of variation across grade levels

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

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

Learning deficits are larger in poorer countries

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

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

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

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

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

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

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

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

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

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

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

Eligibility criteria

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

Search strategy and study identification

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

Data extraction

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

Measurement and standardizationr

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

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

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

Level of education

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

Country income level

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

Data synthesis

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


We prospectively registered a protocol of our systematic review and meta-analysis in the International Prospective Register of Systematic Reviews (CRD42021249944) on 19 April 2021 ( ).

Reporting summary

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

Data availability

The data used in the analyses for this manuscript were compiled by the authors based on the studies identified in the systematic review. The data are available on the Open Science Framework repository ( ). For our systematic review, we searched the following databases: Coronavirus Research Database ( ), Education Resources Information Centre database ( ), International Bibliography of the Social Sciences ( ), Politics Collection ( ), Social Science Database ( ), Sociology Collection ( ), Cumulative Index to Nursing and Allied Health Literature ( ) and Web of Science ( ). We also searched the following preprint and working paper repositories: Social Science Research Network ( ), Munich Personal RePEc Archive ( ), IZA ( ), National Bureau of Economic Research ( ), OSF Preprints ( ), PsyArXiv ( ), SocArXiv ( ) and EdArXiv ( ).

Code availability

All code needed to replicate our findings is available on the Open Science Framework repository ( ).

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

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challenges for education during the pandemic an overview of literature

Learning from a Pandemic. The Impact of COVID-19 on Education Around the World

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challenges for education during the pandemic an overview of literature

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This introductory chapter sets the stage for the book, explaining the goals, methods, and significance of the comparative study. The chapter situates the theoretical significance of the study with respect to research on education and inequality, and argues that the rare, rapid, and massive change in the social context of schools caused by the pandemic provides a singular opportunity to study the relative autonomy of educational institutions from larger social structures implicated in the reproduction of inequality. The chapter provides a conceptual educational model to examine the impact of COVID-19 on educational opportunity. The chapter describes the evolution of the COVID-19 pandemic and how it resulted into school closures and in the rapid deployment of strategies of remote education. It examines available evidence on the duration of school closures, the implementation of remote education strategies, and known results in student access, engagement, learning, and well-being.

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COVID-19 causes unprecedented educational disruption: Is there a road towards a new normal?

1.1 introduction.

The COVID-19 pandemic shocked education systems in most countries around the world, constraining educational opportunities for many students at all levels and in most countries, especially for poor students, those otherwise marginalized, and for students with disabilities. This impact resulted from the direct health toll of the pandemic and from indirect ripple effects such as diminished family income, food insecurity, increased domestic violence, and other community and societal effects. The disruptions caused by the pandemic affected more than 1.7 billion learners, including 99% of students in low and lower-middle income countries (OECD, 2020c; United Nations, 2020 , p. 2).

While just around 2% of the world population (168 million people as of May 27, 2021) had been infected a year after the coronavirus was first detected in Wuhan, China, and only 2% of those infected (3.5 million) had lost their lives to the virus (World Health Organization, 2021a ), considerably more people were impacted by the policy responses put in place to contain the spread of the virus. Beyond the infections and fatalities reported as directly caused by COVID-19, analysis of the excess mortality since the pandemic outbreak, suggests that an additional 3 million people may have lost their lives to date because of the virus (WHO, 2021b ).

As the General Director of the World Health Organization declared the outbreak of COVID-19 a Public Health Emergency of International Concern (PHEIC) on January 30, 2020 (WHO, 2020a ), countries began to adopt a range of policy responses to contain the spread of the virus. The adoption of containment practices accelerated as the COVID-19 outbreak was declared a global pandemic on March 11, 2020 (WHO, 2020b ).

Chief among those policy responses were the social distancing measures which reduced the ability of many people to work, closed businesses, and reduced the ability to congregate and meet for a variety of purposes, including teaching and learning. The interruption of in-person instruction in schools and universities limited opportunities for students to learn, causing disengagement from schools and, in some cases, school dropouts. While most schools put in place alternative ways to continue schooling during the period when in-person instruction was not feasible, those arrangements varied in their effectiveness, and reached students in different social circumstances with varied degrees of success.

In addition to the learning loss and disengagement with learning caused by the interruption of in-person instruction and by the variable efficacy of alternative forms of education, other direct and indirect impacts of the pandemic diminished the ability of families to support children and youth in their education. For students, as well as for teachers and school staff, these included the economic shocks experienced by families, in some cases leading to food insecurity, and in many more causing stress and anxiety and impacting mental health. Opportunity to learn was also diminished by the shocks and trauma experienced by those with a close relative infected by the virus, and by the constraints on learning resulting from students having to learn at home, and from teachers having to teach from home, where the demands of schoolwork had to be negotiated with other family necessities, often sharing limited space and, for those fortunate to have it, access to connectivity and digital devices. Furthermore, the prolonged stress caused by the uncertainty over the evolution and conclusion of the pandemic and resulting from the knowledge that anyone could be infected and potentially lose their lives, created a traumatic context for many that undermined the necessary focus and dedication to schoolwork. These individual effects were reinforced by community effects, particularly for students and teachers living in communities where the multifaceted negative impacts resulting from the pandemic were pervasive.

Beyond these individual and community effects of the pandemic on students, and on teachers and school staff, the pandemic also impacted education systems and schools. Burdened with multiple new demands for which they were unprepared, and in many cases inadequately resourced, the capacity of education leaders and administrators, who were also experiencing the previously described stressors faced by students and teachers, was stretched considerably. Inevitably, the institutional bandwidth to attend to the routine operations and support of schools was diminished and, as a result, the ability to manage and sustain education programs was hampered. Routine administrative efforts to support school operations as well as initiatives to improve them were affected, often setting these efforts back.

Published efforts to take stock of the educational impact of the pandemic to date, as it continues to unfold, have largely consisted of collecting and analyzing a limited number of indicators such as enrollment, school closures, or reports from various groups about the alternative arrangements put in place to sustain educational opportunity, including whether, when, and how schools were open for in-person instruction and what alternative arrangements were made to sustain education remotely. Often these data have been collected in samples of convenience, non-representative, further limiting the ability to obtain true estimates of the education impact of the pandemic on the student population. A recent review of research on learning loss during the pandemic identified only eight studies, all focusing on OECD countries which experienced relatively short periods of school closures (Belgium, the Netherlands, Switzerland, Spain, the United States, Australia, and Germany). These studies confirm learning loss in most cases and, in some, increases in educational inequality, but they also document heterogeneous effects of closures on learning for various school subjects and education levels (Donelly & Patrinos, 2021 ).

There have also been predictions of the likely impact of the pandemic, consisting mostly of forecasts and simulations based on extrapolations of what is known about the interruption of instruction in other contexts and periods. For example, based on an analysis of the educational impact of the Ebola outbreaks, Hallgarten identified the following likely drivers of school dropouts during COVID-19: (1) the reduction in the availability of education services, (2) the reduction in access to education services, (3) the reduction in the utilization of schools, and (4) lack of quality education. Undergirding these drivers of dropout are these factors: (a) school closures, (b) lack of at-home educational materials, (c) fear of school return and emotional stress caused by the pandemic, (d) new financial hardships leading to difficulties paying fees, or to children taking up employment, (e) lack of reliable information on the evolution of the pandemic and on school reopenings, and (f) lack of teacher training during crisis. (Hallgarten, 2020 , p. 3).

Another type of estimate of the likely educational cost of the pandemic includes forecasts of the future economic costs for individuals and for society. A simulation of the impact of a full year of learning loss estimated it as a 7.7% decline in discounted GDP (Hanushek & Woessman, 2020 ). The World Bank estimated the cost of the education disruption as a $10 trillion dollars in lost earnings over time for the current generation of students (World Bank, 2020 ).

Many of the reports to date of the educational responses to the pandemic and their results are in fact reports of intended policy responses, often reflecting the views of the highest education authorities in a country, a view somewhat removed from the day-to-day realities of teachers and students and that provides information about policy intent rather than on the implementation and actual effect of those policies. For instance, the Inter-American Development Bank conducted a survey of the strategies for education continuity adopted by 25 countries in Latin America and the Caribbean during the first phase of the crisis, concluding that most had relied on the provision of digital content on web-based portals, along with the use of TV, radio, and printed materials, and that very few had integrated learning management systems, and only one country had kept schools open (Alvarez et al., 2020 ).

These reports, valuable as they are, are limited in what they contribute to understanding the ways in which education systems, teachers, and students were impacted by the pandemic and about how they responded, chiefly because it is challenging to document the impact of an unexpected education emergency in real time, and because it will take time to be able to ascertain the full short- and medium-term impact of this global education shock.

1.2 Goals and Significance of this Study

This book is a comparative effort to discern the short-term educational impact of the pandemic in a selected number of countries, reflecting varied levels of financial and institutional education resources, a variety of governance structures, varied levels of education performance, varied regions of the world, and countries of diverse levels of economic development, income per capita, and social and economic inequality. Our goal is to contribute an evidence-based understanding of the short-term educational impact of the pandemic on students, teachers, and systems in those countries, and to discuss the likely immediate effects of such an impact. Drawing on thirteen national case studies, a chapter presenting a comparative perspective in five OECD countries and another offering a global comparative perspective, we examine how the pandemic impacted education systems and educational opportunity for students. Such systematic stock-taking of how the pandemic impacted education is important for several reasons. The first is that an understanding of the full global educational impact of the pandemic necessitates an understanding of the ways in which varied education systems responded (such as the nature and duration of school closures, alternative means of education delivery deployed, and the goals of those strategies of education continuity during the pandemic) and of the short-term results of those responses (in terms of school attendance, engagement, learning and well-being for different groups of students). In order to understand the possible student losses in knowledge and skills, or in educational attainment that the current cohort of students will experience relative to previous or future cohorts, and to understand the consequences of such losses, we must first understand the processes through which the pandemic influenced their opportunities to learn. Such systematization and stock-taking are also essential to plan for remediation and recovery, in the immediate aftermath of the pandemic and beyond. While the selection of countries was not intended to represent the entire world, the knowledge gained from the analysis of the educational impact of the pandemic on these diverse cases, as well as making visible what is not yet known, will likely have heuristic value to educators designing mitigation and remediation strategies in a wide variety of settings and may provide a useful framework to design further research on this topic.

In addition, the pandemic is likely to exacerbate preexisting challenges and to create new ones, increasing unemployment for instance or contributing to social fragmentation, which require education responses. Furthermore, there were numerous education challenges predating the pandemic that need attention. Addressing these new education imperatives, as well as tackling preexisting ones, requires ‘building back better’; not just restoring education systems to their pre-pandemic levels of functioning, but rather realigning them to these new challenges. Examining the short-term education response to the pandemic provides insight into whether the directionality of such change is aligned to ‘building back better’ and with the kind of priorities that should guide those efforts during the remainder of the pandemic and in the pandemic aftermath.

Lastly, the pandemic provides a rare opportunity to help us understand how education institutions relate to other institutions and to their external environment under conditions of rapid change. Much of what we know about the relationship of schools to their external environment is based on research carried out in much more stable contexts, where it is difficult to discern what is a cause and what is an effect. For instance, there is robust evidence that schools often reflect and contribute to reproducing social stratification, providing children from different social origins differential opportunities to learn, and resulting in children of poor parents receiving less and lower quality schooling than children of more affluent parents. It is also the case that educational attainment is a robust predictor of income. Increases in income inequality correlate with increases in education inequality, although government education policies have been shown to mitigate such a relationship (Mayer, 2010 ).

The idea that education policy can mitigate the structural relationship between education and income inequality suggests that the education system has certain autonomy from the larger social structure. But disentangling to what extent school policy and schools can just reproduce social structures or whether they can transform social relations is difficult because changes in education inequality and social inequality happen concurrently and slowly, which makes it difficult to establish what is cause and what is effect. However, a pandemic is a rare rapid shock to that external environment, the equivalent of a solar eclipse, and thus a singular opportunity to observe how schools and education systems respond when their external environment changes, quite literally, overnight. Such a shock will predictably have disproportionate impacts on the poor, via income and health effects, presenting a unique opportunity to examine whether education policies are enacted to mitigate the resulting disproportionate losses on educational opportunity from such income and health shocks for the poor and to what extent they are effective.

1.3 A Stylized Global Summary of the Facts

A full understanding of the educational impact of the pandemic on systems, educators, and students will require an analysis of such impact in three time frames: the immediate impact, taking place while the pandemic is ongoing; the immediate aftermath, as the epidemic comes under control, largely as a result of the population having achieved herd immunity after the majority has been inoculated; and the medium term aftermath, once education systems, societies, and economies return to some stability. Countries will differ in the timeline at which they transition through these three stages, as a function of the progression of the pandemic and success controlling it, as a result of public health measures and availability, distribution, and uptake of vaccines, and as a result of the possible emergence of new more virulent strands of the virus which could slow down the efforts to contain the spread. There are challenges involved in scaling up the production and distribution of vaccines, which result in considerable inequalities in vaccination rates among countries of different income levels. It is estimated that 11 billion doses of vaccines are required to achieve global herd immunity (over 70% of the population vaccinated). By May 24, 2021, a total of 1,545,967,545 vaccine doses had been administered (WHO, 2021a ), but 75% of those vaccines have been distributed in only 10 high income countries (WHO, 2021c ).

Of the 9.5 billion doses expected to be available by the end of 2021, 6 billion doses have already been purchased by high and upper middle-income countries, whereas low- and lower-income countries—where 80% of the world population lives—have only secured 2.6 billion, including the pledges to COVAX, an international development initiative to vaccinate 20% of the world population (Irwin, 2021 ). At this rate, it is estimated that it will take at least until the end of 2022 to vaccinate the lowest income population in the world (Irwin, 2021 ).

The educational impact of the pandemic in each of these timeframes will likely differ, as will the challenges that educators and administrators face in each case, with the result that the necessary policy responses will be different in each case. The immediate horizon—what could be described as the period of emergency—can in turn be further analyzed in various stages since, given the relatively long duration of the pandemic, spanning over a year, schools and systems were able to evolve their responses in tandem with the evolution of the epidemic and continued to educate to varying degrees as a result of various educational strategies of education continuity adopted during the pandemic. During the initial phase of this immediate impact, the responses were reactive, with very limited information on their success, and with considerable constraints in resources available to respond effectively. This initial phase of the emergency was then followed by more deliberate efforts to continue to educate, in some cases reopening schools—completely or in part—and by more coordinated and comprehensive actions to provide learning opportunities remotely. The majority of the analysis presented in this book focuses on this immediate horizon, spanning the twelve months between January of 2020, when the pandemic was beginning to extend beyond China, as the global outbreak was recognized on March 11, through December of 2020.

The pandemic’s impact in the immediate aftermath and beyond will not be a focus of this book, largely because most countries in the world have not yet reached a post-pandemic stage, although the concluding chapter draws out implications from the short-term impact and responses for that aftermath.

Education policy responses need to differentially address each of these three timeframes: short-term mitigation of the impact during the emergency; immediate remediation and recovery in the immediate aftermath; and medium-term recovery and improvement after the initial aftermath of the pandemic.

As the epidemic spread from Wuhan, China—where it first broke out in December of 2019—throughout the world, local and national governments suspended the operation of schools as a way to contain the rapid spread of the virus. Limiting gatherings in schools, where close proximity would rapidly spread respiratory infections, had been done in previous pandemics as a way to prevent excess demand for critical emergency services in hospitals. Some evidence studying past epidemics suggested in fact that closing schools contributed to slow down the spread of infections. A study of non-pharmaceutical interventions adopted during the 1918–19 pandemic in the United States shows that mortality was lower in cities that closed down schools and banned public gatherings (Markel et al., 2007 ). A review of 79 epidemiological studies, examining the effect of school closures on the spread of influenza and pandemics, found that school closures contributed to contain the spread (Jackson et al., 2013 ).

In January 26, China was the first country to implement a national lockdown of schools and universities, extending the Spring Festival. As UNESCO released the first global report on the educational impact of the pandemic on March 3, 2020, twenty-two countries had closed schools and universities as part of the measures to contain the spread of the virus, impacting 290 million students (UNESCO, 2020 ). Following the World Health Organization announcement, on March 11, 2020, that COVID-19 was a global pandemic, the number of countries closing schools increased rapidly. In the following days 79 countries had closed down schools (UNESCO, 2020 ).

Following the initial complete closure of schools in most countries around the world there was a partial reopening of schools, in some cases combined with localized closings. By the end of January 2021, UNESCO estimated that globally, schools had completely closed an average of 14 weeks, with the duration of school closures extending to 22 weeks if localized closings were included (UNESCO, 2021 ). There is great variation across regions in the duration of school closures, ranging from 20 weeks of complete national closings in Latin America and the Caribbean to just one month in Oceania, and 10 weeks in Europe. There is similar variation with respect to localized closures, from 29 weeks in Latin America and the Caribbean to 7 weeks in Oceania, as seen in Fig.  1.1 . By January 2021, schools were fully open in 101 countries.

A multi-line graph of duration in weeks versus months from March 2020 to January 2021. It depicts a rise in the duration in 7 regions along with the World. Latin America and the Caribbean lead among all.

Source UNESCO ( 2021 )

Duration of complete and partial school closures by region by January 25, 2021.

As it became clear that it would take considerable time until a vaccine to prevent infections would become available, governments began to consider options to continue to educate in the interim. These options ranged from total or partial reopening of schools to creating alternative means of delivery, via online instruction, distributing learning packages, deploying radio and television, and using mobile phones for one- or two-way communication with students. In most cases, deploying these alternative means of education was a process of learning by doing, sometimes improvisation, with a rapid exchange of ideas across contexts about what was working well and about much that was not working as intended. As previous experience implementing these measures in a similar context of school lockdown was limited, there was not much systematized knowledge about what ‘worked’ to transfer any approach with some confidence of what results it would produce in the context created by the pandemic. As these alternatives were put in place, educators and governments learned more about what needs they addressed, and about which ones they did not.

For instance, it soon became apparent that the creation of alternative ways to deliver instruction was only a part of the challenge. Since in many jurisdictions schools deliver a range of services—from food to counseling services—in addition to instruction, it became necessary to find alternative ways to deliver those services as well, not just to meet recognized needs prior to the pandemic but because the emergency was increasing poverty, food insecurity, and mental health challenges, making such support services even more essential.

As governments realized that the alternative arrangements to deliver education had diminished the capacity to achieve the instructional goals of a regular academic year, it became necessary to reprioritize the focus of instruction.

In a study conducted at the end of April and beginning of May 2020, based on a survey administered to a haphazard sample of teachers and education administrators in 59 countries, we found that while schools had been closed in all cases, plans for education continuity had been implemented in all countries we had surveyed. Those plans involved using existing online resources, online instruction delivered by students’ regular teachers, instructional packages with printed resources, and educational television programmes. The survey revealed severe disparities in access to connectivity, devices, and the skills to use them among children from different socio-economic backgrounds. On balance, however, the strategies for educational continuity were rated favourably by teachers and administrators, who believed they had provided effective opportunities for student learning. These strategies prioritized academic learning and provided support for teachers, whereas they gave less priority to the emotional and social development of students.

These strategies deployed varied mechanisms to support teachers, primarily by providing them access to resources, peer networks within the school and across schools, and timely guidance from leadership. A variety of resources were used to support teacher professional development, mostly relying on online learning platforms, tools that enabled teachers to communicate with other teachers, and virtual classrooms (Reimers & Schleicher, 2020 ).

Some countries relied more heavily on some of these approaches, while others used a combination, as reported by UNESCO and seen in Fig.  1.2 .

A Venn diagram overlaps online and T V or radio distance solutions with 95 and 98 governments and 999 and 104 m i students, respectively. Governments and m i students with both are 29 and 122 and with no information are 38 and 54.

Source Giannini ( 2020 )

Government-initiated distance learning solutions and intended reach.

A stacked bar graph of % of students versus 7 regions for connected and not connected internet. In Sub-Saharan Africa, 80% of students has no internet, while in Eastern Europe and Central Asia, 80% of students has internet.

Share of students with Internet at home in countries relying exclusively on online learning platforms.

A significant number of children did not have access to the online solutions provided because of lack of connectivity, as shown in a May 2020 report by UNESCO. In Sub-Saharan Africa, a full 80% of children lacked internet at home; this figure was 49% in Asia Pacific; 34% in the Arab States and 39% in Latin America, but it was only 20% in Eastern Europe and Central Asia and 14% in Western Europe and North America (Giannini, 2020 ).

Similar results were obtained by a subsequent cross-national study administered to senior education planning officials in ministries of education, conducted by UNESCO, UNICEF, and the World Bank. These organizations administered two surveys between May and June 2020, and between July and October 2020, to government officials in 118 and 149 countries, respectively. The study documented extended periods of school closures. The study further documented differences among countries in whether student learning was monitored, with much greater levels of monitoring in high income countries than in lower income countries.

The study also confirmed that most governments created alternative education delivery systems during the period when schools were closed, through a variety of modalities including online platforms, television, radio, and paper-based instructional packages. Governments also adopted targeted measures to support access to these platforms for disadvantaged students, provided devices or subsidized connectivity, and supported teachers and caregivers. The report shows disparities between countries at different income levels, with most high-income countries providing such support and a third of lower income countries not providing any specific support for connectivity to low-income families (UNESCO-UNICEF-the World Bank, 2020 ).

The UNESCO-UNICEF-World Bank surveys reveal considerable differences in the education responses by level of income of the country. For instance, whereas by the end of September of 2020 schools in high-income countries had been closed 27 days, on average, that figure increased to 40 days in middle-income countries, to 68 days in lower middle-income countries, and to 60 days in low-income countries (Ibid, 15).

For most countries there were no plans to systematically assess levels of students’ knowledge and skills as schools reopened, and national systematic assessments were suspended in most countries. There was considerable variation across countries, and within countries, in terms of when schools reopened and how they did so. Whereas some countries offered both in-person and remote learning options—and gave students a choice of which approach to use—others did not offer choices. There were also variations in the amount of in-person instruction students had access to once schools reopened. Some schools and countries introduced measures to remediate learning loss as schools reopened, but not all did.

1.4 The Backdrop to the Pandemic: Enormous and Growing Inequality and Social Exclusion

The pandemic impacted education systems as they faced two serious interrelated preexisting challenges: educational inequality and insufficient relevance. A considerable growth in economic inequality, especially among individuals within the same nations, has resulted in challenges of social inclusion and legitimacy of the social contract, particularly in democratic societies. Over the last thirty years, income inequality has increased in countries such as China, India, and most developed countries. Over the last 25 years there are also considerable inequalities between nations, even though those have diminished over the last 25 years. The average income of a person in North America is 16 times greater than the income of the average person in Sub-Saharan Africa. 71% of the world’s population live in countries where inequality has grown (UN, 2021 ). The Great Recession of 2008–2009 worsened this inequality (Smeedling, 2012 ).

One of the correlates of income inequality is educational inequality. Studies show that educational expansion (increasing average years of schooling attainment and reducing inequality of schooling) relates to a reduction in income inequality (Coadi & Dizioly, 2017 ). But education systems, more often than not, reflect social inequalities, as they offer the children of the poor, often segregated in schools of low quality, deficient opportunities to learn skills that help them improve their circumstances, whereas they provide children from more affluent circumstances opportunities to gain knowledge and skills that give them access to participate economically and civically. In doing so, schools serve as a structural mechanism that reproduces inequality, and indeed legitimize it as they obscure the structural forces that sort individuals into lives of vastly different well-being with an ideology of meritocracy that in effect blames the poor for the circumstances that their lack of skills lead to, when they have not been given effective opportunities to develop such skills.

There is abundant evidence of the vastly different learning outcomes achieved by students from different social origins, and of the differences in the educational environments they have access to. In the most recent assessment of student knowledge and skills conducted by the Organization for Economic Cooperation and Development (OECD), the socioeconomic status of students is significantly correlated to student achievement in literacy, math, and science in all 76 countries participating in the study (OECD, 2019 ). On average, among OECD countries, 12% of the variance in reading performance is explained by the socioeconomic background of the student. The strength of this relationship varies across countries, in some of them it is lower than the average as is the case in Macao (1.7%), Azerbaijan (4.3%), Kazakhstan (4.3%), Kosovo (4.9%), Hong Kong (5.1%), or Montenegro (5.8%). In other countries, the strength of the relationship between socioeconomic background and reading performance is much greater than the average such as in Belarus (19.8%), Romania (18.1%), Philippines (18%), or Luxembourg (17.8%). A significant reading gap exists between the students in the bottom 25% and those in the top 25% of the socioeconomic distribution, averaging 89 points, which is a fifth of the average reading score of 487, and almost a full standard deviation of the global distribution of reading scores in PISA. In spite of these strong associations between social background and reading achievement, there are students who defy the odds; the percentage of students whose social background is at the bottom 25%—the poorest students—whose reading performance is in the top 25%—academically resilient students—averages 11% across all OECD countries. This percentage is much greater in the countries where the relationship between social background and achievement is lower. In Macao, for instance, 20% of the students in the top 25% of achievement are among the poorest 25%. In contrast, in countries with a strong relationship between socioeconomic background and reading achievement, the percentage of academically resilient students among the poor is much lower, in Belarus and Romania it is 9%. These differences in reading skills by socioeconomic background are even more pronounced when looking at the highest levels of reading proficiency, those at which students can understand long texts that involve abstract and counterintuitive concepts as well as distinguish between facts and opinions based on implicit clues about the source of the information. Only 2.9% of the poorest students, compared with 17.4% among the wealthier quarter, can read at those levels of proficiency on average for the OECD (OECD, 2019b , p. 58). Table 1.1 summarizes socioeconomic disparities in reading achievement. The relationship of socioeconomic background to students’ knowledge and skills is stronger for math and science. On average, across the OECD, 13.8% of math skills and 12.8% of science skills are predicted by socioeconomic background.

The large number of children who fail to gain knowledge and skills in schools has been characterized, by World Bank staff and others, as ‘a global learning crisis’ or ‘learning poverty’, though the evidence on the strong correlation of learning poverty to family poverty suggests that this should more aptly be characterized as ‘the learning crisis for the children of the poor’ (World Bank, 2018 ). These low levels of learning have direct implications for the ability of students to navigate the alternative education arrangements put in place to educate during the pandemic; clearly students who can read at high levels are more able to study independently through texts and other resources than struggling readers.

The second interrelated challenge is that of ensuring that what ALL children learn in school is relevant to the challenges of the present and, most importantly, of the future. While the challenge of the relevance of learning is not new in education, the rapid developments in societies, resulting from technologies and politics, create a new urgency to address it. For students with the capacity to set personal learning goals, or with more self-management skills, or with greater skills in the use of technology, or with greater flexibility and resiliency, or with prior experience with distance learning, it was easier to continue to learn through the remote arrangements established to educate during the pandemic than it was for students with less developed skills in those domains. While the emphasis on the development of such breadth of skills, also called twenty-first century skills, has been growing around the world, as reflected in a number of recent curriculum reforms, there are large gaps between the ambitious aspirations reflected in modern curricula and standards, and the implementation of those reforms and instructional practice (Reimers, 2020b ; Reimers, 2021 ).

The challenges of low efficacy and relevance have received attention from governments and from international development agencies, including the United Nations and the OECD. The UN Sustainable Development Goals, for instance, propose a vision for education that aligns with achieving an inclusive and sustainable vision for the planet, even though, by most accounts, the resources deployed to finance the achievement of the education goal fall short with respect to those ambitions. In 2019 UNESCO’s director general tasked an international commission with the preparation of a report on the Futures of Education, focusing in particular on the question of how to align education institutions with the challenges facing humanity and the planet.

1.5 The Pandemic and Health

The main direct effect of the Coronavirus disease is in infecting people, compromising their health and in some cases causing their death. By May 27 of 2021, 168,040,871 people worldwide had become infected, of whom 3,494,758 had died reportedly from COVID-19 (World Health Organization, 2021a ) and an additional 3 million had likely died from COVID-19 as they were excess deaths relative to the total number of deaths the previous year (World Health Organization, 2021b ). As expected, more people are infected in countries with larger populations, but the rate of infection by total population and the rate of deaths by total population suggest variations in the efficacy of health policies used to contain the spread as shown in Fig.  1.4 , which includes the top 20 countries with the highest relative number of COVID-19 fatalities. These differences reflect differences in the efficacy of health policies to contain the pandemic, as well as differences in the response of the population to guidance from public health authorities. Countries in which political leaders did not follow science-based advice to contain the spread, and in which a considerable share of the population did not behave in ways that contributed to mitigate the spread of the virus, not wearing face masks or socially distancing for instance, such as in Brazil and the United States, fared much poorer than those who did implement effective public health containment measures such as China, South Korea, or Singapore, with such low numbers of deaths per 100,000 people that they are not even on this chart of the top 20.

A horizontal bar graph of 20 countries versus deaths per 100,000 population. The highest value of 215.32 is in Brazil, and the lowest is 9.96 in Japan.

Source Johns Hopkins University. Coronavirus Resource Center ( 2021 )

Number of reported COVID-19 deaths per 100,000 population in the 20 countries with the highest rates as of May 27, 2021.

1.6 The Pandemic, Poverty, and Inequality

The social distancing measures limited the ability of business to operate, reducing household income and demand. This produced an economic recession in many countries. For example, in the United States, 43% of small businesses closed temporarily (Bartik et al., 2020).

A household survey in seventeen countries in Latin America and the Caribbean demonstrates that the COVID-19 pandemic differentially impacted households at different income levels. The study shows significant and unequal job losses with stronger effects among the lowest income households. The study revealed that 45% of respondents reported that a member of their household had lost a job and that, for those owning a small family business, 58% had a household member who had closed their business. These effects are considerably more pronounced among the households with lower incomes, with nearly 71 percent reporting that a household member lost their job and 61 percent reporting that a household member closed their business compared to only 14 percent who report that a household member lost their job and 54 percent reporting that a household member closed their business among those households with higher incomes (Bottan et al., 2020 ).

It is estimated that the global recession augmented global extreme poverty by 88 million people in 2020, and an additional 35 million in 2021 (World Bank, 2020 ). A survey conducted by UNICEF in Mexico documented a 6.7% increase in hunger and a 30% loss in household income between May and July of 2020 (UNICEF México, 2020 ).

Because schools in some countries offer a delivery channel for meals as part of poverty reduction programming, several countries created alternative arrangements during the pandemic to deliver those or replaced them with cash transfer programs. Sao Paulo, Brazil, for instance, created a cash transfer program “ Merenda en Casa ’’ to replace the daily meal school programs (Dellagnelo & Reimers, 2020 ; Sao Paulo Government, 2020 ).

In the summer of 2020, Save the Children conducted a survey of children and families in 46 countries to examine the impact of the crisis, focusing on participants in their programs, other populations of interest, and the general public. The report of the findings for program participants—which include predominantly vulnerable children and families—documents violence at home, reported in one third of the households. Most children (83%) and parents (89%) reported an increase in negative feelings due to the pandemic and 46% of the parents reported psychological distress in their children. For children who were not in touch with their friends, 57% were less happy, 54% were more worried, and 58% felt less safe. For children who could interact with their friends less than 5% reported similar feelings. Children with disabilities showed an increase in bed-wetting (7%) and unusual crying and screaming (17%) since the outbreak of the pandemic, an increase three times greater than for children without disabilities. Children also reported an increase in household chores assigned to them, 63% for girls and 43% for boys, and 20% of the girls said their chores were too many to be able to devote time to their studies, compared to 10% of boys (Ritz et al., 2020 ).

1.7 Readiness for Remote Teaching During a Pandemic

Countries varied in the extent to which they had, prior to the pandemic, supported teachers and students in developing the capacities to teach and to learn online, and they varied also in the availability of resources which could be rapidly deployed as part of the remote strategy of educational continuity. Table 1.2 shows the extent to which teachers were prepared to use Information and Communication Technologies (ICT) in their teaching based on a survey administered by the OECD in 2018. The percentage of teachers who report that the use of ICT was part of their teacher preparation ranges from 37 to 97%. There is similar variation in the percentage of teachers who feel adequately prepared to use ICT, or who have received recent professional development in ICT, or who feel a high need for professional development in ICT. There is also quite a range in the percentage of teachers who regularly allow students to use ICT as part of their schoolwork.

This variation, along with variation in availability of technology and connectivity among students, creates very different levels of readiness to teach remotely online as part of the strategy of educational continuity during the interruption of in-person instruction.

1.8 What are the Short-term Educational Impacts of the Pandemic?

The study of the ways in which the pandemic can be expected to influence the opportunity to learn can be based on what is known about the determinants of access to school and learning, drawing on research predating the pandemic.

Opportunity to learn can be usefully disaggregated into opportunity to access and regularly attend school, and opportunity to learn while attending and engaging in school. John Carroll proposed a model for school learning which underscored the primacy of learning time. In his model, learning is a function of time spent learning relative to time needed to learn. This relationship between aptitude (time needed to learn) and learning is mediated by opportunity to learn (amount of time available for learning), ability to understand instruction, quality of instruction, and perseverance (Carroll, 1963 ).

In a nutshell, the pandemic limited student opportunity for interactions with peers and teachers and for individualized attention—decreasing student engagement, participation, and learning—while augmenting the amount of at-home work which, combined with greater responsibilities and disruptions, diminished learning time while increasing stress and anxiety, and for some students, aggravated mental health challenges. The pandemic also increased teacher workload and stress while creating communication and organizational challenges among school staff and between them and parents.

Clearly the pandemic constrained both the home conditions and the school conditions that support access to school, regular attendance, and time spent learning. The alternative strategies deployed to sustain the continuity of schooling in all likelihood only partially restored opportunity to learn and quality of instruction. Given the lower access that disadvantaged students had to technology and connectivity, and the greater likelihood that their families were economically impacted by the pandemic, it should be expected that their opportunities to learn were disproportionately diminished, relative to their peers with more access and resources and less stressful living conditions.

As a result of these constraints on opportunity to learn, the most vulnerable students were more likely to disengage from school. Such disengagement is, in effect, a form of school dropout, at least temporarily. As students fall behind because of their lack of engagement, this further diminishes their motivation, leading to more disengagement. It is possible that such a form of temporary dropout may lead to permanent dropout as learners take on other roles, and as learning recovery and catch up become more difficult as they fall further behind in terms of curricular expectations. The children who drop out will add to the already large number of children out of school, 258 million in 2018 (UNESCO, 2018 ). UNESCO has estimated that 24 million children are at risk of not returning to school (UNESCO, 2020a ) which would bring the total number of out of school children to the same level as in the year 2000, in effect wiping out two decades of progress in educational access (UNESCO, 2020c , 2). These estimates are based on the following likely processes: (a) educational and socioemotional disengagement, (b) increased economic pressure, and (c) health issues and safety concerns (UNESCO, 2020a ).

In addition to the direct impact of the health and economic shocks on student engagement, the lack of engagement of students was a function of the inadequacy of government efforts to sustain education through alternative means and the circumstances of students. In Mexico, for instance, the Federal Ministry of Education in Mexico closed schools on March 23, 2020; these closures remained in effect for at least a year. When the academic year began on August 24, 2020, the government deployed a national strategy for education continuity consisting of remote learning through television, complemented by access to digital platforms such as Google and local radio educational programming, with programs of teacher professional development on basic ICT skills to engage students remotely (World Bank, 2020c ; SEP, Boletín 101, 2020 ). A television strategy was adopted for education continuity during the pandemic since only 56.4% of households have internet access, while 92.5% have a television (INEGI, 2019) and Mexico has a long-standing program of TV secondary school (Ripani & Zucchetti, 2020 ). Since March 2020, educational television content was delivered through Aprende en Casa I, II, and III (Learning at Home). Some Mexican states complemented the national strategy with additional measures, such as radio programs and textbook distribution, which were planned locally (World Bank, 2020c ). Indigenous communities were also reached in 15 indigenous languages through partnerships with local radio networks (Ripani & Zucchetti, 2020 ). The State of Quintana Roo, for example, which has a large Mayan population, produced and distributed educational workbooks for students on various subjects written both in Spanish and Mayan languages (SEQ, 2020 ). The State Secretary of Education also created a YouTube channel with video lessons and a public television channel, within Quintana Roo’s Social Communication system, that was solely dedicated to the distribution of educational content (Gonzáles, 2020 ; Hinckley et al., 2021 ).

While the choice of a TV-based strategy for education continuity was predicated on the almost universal accessibility to television, and on a long tradition of the Ministry of Education producing educational TV ( Telesecundaria ), a survey conducted in June 2020 by an agency of the Mexican government showed that 57.3% of the students lacked access to a computer, television, radio, or cell phone during the emergency and 52.8% of the strategies required materials that students did not have in their homes (MEJOREDU, 2020a ). In the same survey, 51.4% of students reported that the activities online, on the TV, and on radio programs were boring (MEJOREDU, 2020a ). Students reported challenges to learning stemming from limited support or lack of explanations from their teachers, lack of clarity in the activities they were supposed to carry out, limited feedback on the work completed, lack of knowledge about their successes or mistakes in the activities, insufficient understanding of what they were doing, less learning and understanding, and perception of not having the necessary knowledge to pass onto the next grade. More than half of the students (60% at the primary level and 44% at the secondary level) indicated that during the period of remote learning they had simply reviewed previously taught content (MEJOREDU, 2020a ).

The same study canvassed teachers for their views on factors which prevented student engagement, 84.6% of the teachers mentioned lack of internet access, 76.3% mentioned lack of electronic devices to access activities, and 73.3% mentioned limited economic resources (MEJOREDU, 2020a , p. 10). Students, in turn, reported the following as factors which excluded them: difficulty in following the activities (“it’s difficult,” “I don’t understand,” “I don’t have time”) followed by stress or frustration, the need to attend to housework, obligation to take care of other people, and lack of motivation expressed as laziness, tiredness, boredom, loss of interest, or discouragement. Half of the students reported that the tasks involved in learning remotely caused stress and 40% reported sadness and low levels of motivation (MEJOREDU, 2020a , p. 10).

Mexico’s approach to education continuity is illustrative of the approach followed by many other countries. Costa Rica, for example, also closed down schools upon the declaration of a national emergency in March 2020, transitioning to a virtual school program, delivered through an online program, and a distance learning program that varied throughout different cantons in the country (Diaz Rojas, 2020 ). These were supplemented by an educational television program of two hours a day during weekdays for students in the upper elementary grades, a daily one-hour radio program augments these efforts. Five months after the initiation of the virtual strategy, 35% of the students had not logged into the free online accounts provided to them by the Ministry (Direccion de Prensa y Relaciones Publicas, 2020 ).

Bangladesh also closed schools on March 16th, 2020, and gradually extended what was to be a two week lock down for at least a year, relying on a distance learning strategy of education continuity relying on internet, TV, radio, and mobile phones, which had serious challenges reaching students in a country where only 13% of the population used the internet in 2019 and only 5.6% of households have access to a computer (World Bank, 2019 ). Access to TV was greater, reaching 56% of the households, but very few had access to radio (0.6% of the population). While access to mobile phones was greater it was not universal, with 92% of families in the lowest wealth quintile with access to mobile phones, but only 19% of the total population with access to a smartphone (Bell et al., 2021 ; World Bank, 2019 ).

Some countries found the prospects of developing alternative forms of education continuity so daunting that they suspended the school year entirely. In Kenya, for instance, by July of 2020 the Ministry of Education had decided to close all public schools in the country until January 2021 and then restart the academic school year. The decision was revised in October of 2020, with a partial reopening of schools for the grades in which students take exams (grade 4, class 8, and form 4) in order to prepare students for the official school-leaving examinations and for critical transitions (Voothaluru et al., 2021 ).

In South Africa, COVID-19 was met by wide-scale school closures, with no practical way to shift to remote learning given lack of student access to the internet (Statistics South Africa, 2019 ; UNICEF, 2020 ). In September 2020, schools reopened after several months of being closed, only to close again in January 2021, during the second wave of the pandemic (UNICEF, 2020 ).

Even well-resourced countries shifted to remote instruction for at least a short period. In the United Arab Emirates, for instance, the Ministry of Education shifted education to remote learning from March to June 2020. Upon resuming in-person instruction at the start of the new academic year, however, families had the discretion to choose whether to participate fully in-person, fully online, or in blended learning modalities. In spite of the strong commitment to inclusion of people with disabilities in the UAE, providing adequate accommodations for them was challenging (Mohajeri et al., 2021 ).

Among the many challenges faced by schools and education systems, as they relied on these alternative forms of educational continuity, was the assessment of students’ knowledge. Many national assessments were cancelled. Absence of information on student knowledge and skills prevented determining the extent of learning loss and the implementation of remedial programs to address it. Other challenges stemmed from teachers’ limited skills in teaching remotely, as shown earlier.

While the lack of reliable assessments of learning loss to date prevent estimating the full impact of the pandemic for most countries in the world, the limited studies available document deep impacts, particularly for disadvantaged students. A recent study conducted in Belgium, where schools were closed for approximately nine weeks, shows significant learning losses in language and math (a decrease in school averages of mathematics scores of 0.19 standard deviations and of Dutch scores of 0.29 standard deviations as compared to the previous cohort) and an increase in inequality in learning outcomes by 17% for math and 20% for Dutch, in part a result of increases in inequality between schools (an increase in between school inequality of 7% for math and 18% for Dutch). Losses are greater for schools with a higher percentage of disadvantaged students (Maldonado, De Witte, 2020 ). A review of this and seven additional empirical studies of learning loss, of which one focused on higher education, finds learning loss also in the Netherlands, the United States, Australia, and Germany, although the amount of learning loss is lower than in the study in Belgium. A study in Switzerland finds learning loss to be insignificant and a study in Spain finds learning gains during the pandemic (Donnelly & Patrinos, 2021 , 149). These seven out of eight studies that identified learning loss were conducted in countries where education systems were relatively well-resourced and covered relatively short periods of school closures: 9 weeks in Belgium, 8 weeks in the Netherlands, 8 weeks in Switzerland, 8–10 weeks in Australia, and 8.5 weeks in Germany (Ibid). The studies also show that while there is consistent learning loss for primary school students, this is not the case for secondary and higher education students.

In addition to the losses in educational opportunity just described, there may be some silver linings resulting from this global education calamity. The first is that the interruption of schooling made visible how important teachers and schools are to support learning, and how many other activities depend on the ability of schools to carry out their role effectively. As teachers had to depend on parents to support students in learning more than is habitual under regular circumstances, this may have created valuable opportunities for mutual recognition between teachers and parents. As each of these groups is now more cognizant of what the other does, perhaps they have learned to collaborate more effectively. Increased parental involvement in the education of their children may have also strengthened important bonds and further developed parenting skills. For some children, it is possible that the freedom from the routines and constraints of schools, and from some of the social pressures resulting from interaction with peers, may have provided opportunities to learn independently and for greater focus, depth, and reflection.

The emergency also made visible the importance of attending to the emotional well-being of students and showed that integrating this as part of the work of schools is not only intrinsically valuable, but also part and parcel of a good education. In attempting to provide emotional support to students, teachers also had to re-prioritize the curriculum, engaging in a valuable exercise of rethinking what is truly important for students to learn. Facing the challenge of reprioritizing the curriculum, some countries embarked on a process of revision for the long haul.

For instance, the South African Directorate of Basic Education has taken a multi-pronged approach to address this complex set of issues. Two such approaches include (1) A short-term―3 year―education recovery plan in response to COVID-19, to address learning loss, and (2) A medium to long-term curriculum modernization plan (2024 onward), aimed at addressing the issue of curriculum relevance and preparing learners for the fast-changing world. The Directorate of Basic Education is working with the National Education Collaboration Trust (NECT) to establish a Competency-Infused Curriculum Task Team (CICTT) mandated to conceptualize and provide a set of policy and implementation recommendations for a modernized curriculum (Eadie et al., 2021 ).

Creating alternative forms of education delivery during the emergency provided an opportunity for innovation and creativity, an opportunity that many teachers took up, demonstrating outstanding professionalism. The organizational conditions which unleashed such creativity and professionalism need to be better understood, as they may represent a valuable dividend generated by this pandemic, which could be usefully carried forward into the future.

1.9 Methods

To contribute to this book, in July of 2020 I invited colleagues from fifteen educational institutions, the majority of whom are university-based researchers in a variety of countries reflecting various regions of the world and varied education systems in terms of the salient challenges facing those systems and the levels of education spending across them. We agreed to conduct case studies that would analyze available empirical evidence to address the questions below. The case studies were conducted between August of 2020 and January of 2021. We then met at a virtual conference in February of 2021 to discuss the draft chapters, and then finalized them by April of 2021 based on feedback received from other contributors to the project.

When did the COVID-19 pandemic reach national attention in the country? Is there a specific date when the government declared a national COVID-19 emergency? What educational policies followed that declaration? Was attendance to school suspended? Where in the school year did this happen –was it the beginning of the school year, the middle or the end?

What policy responses were adopted at various stages during the pandemic to sustain educational opportunity? Were there alternative means of education delivery created? Was the curriculum reprioritized? Were platforms for online learning created? Educational radio? Television? Were there special efforts to support the education of marginalized students?

What is known about the impact of the pandemic on educational opportunities in the country, for different groups of students? Is there evidence on the degree to which children remained enrolled in school, engaged in their studies, learning?

Are there any educational positive effects of the pandemic? Any silver linings? Lessons learned that would be of benefit to education in the future.

What is known about the effects of the alternative means of delivery put in place, if any?

Given current knowledge, what are the likely educational implications of the pandemic?

What are the areas in which more research is needed?

What are areas that merit policy attention during the remaining period of the pandemic, and beyond?

In addition to a chapter with a global focus, and a chapter comparatively examining five OECD countries, the book includes chapters focusing on Brazil, Finland, Japan, Mexico, Norway, Portugal, Russia, Singapore, Spain, South Africa, and the United States. A concluding chapter discusses some of the threads running through the cases and the implications of the findings.

What follows is a rich and complex story. While most children of the world experienced some form of educational interruption, the extent and depth varied among countries and among groups of children. Understanding the details of how education systems were more able to preserve educational opportunity for some children and in some countries is crucial to discern what was lost, what lies ahead, and what we can expect from schools as institutions that can build a future that is better than the present or the past.

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Shifting online during COVID-19: A systematic review of teaching and learning strategies and their outcomes

  • Joyce Hwee Ling Koh   ORCID: 1 &
  • Ben Kei Daniel 1  

International Journal of Educational Technology in Higher Education volume  19 , Article number:  56 ( 2022 ) Cite this article

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This systematic literature review of 36 peer-reviewed empirical articles outlines eight strategies used by higher education lecturers and students to maintain educational continuity during the COVID-19 pandemic since January 2020. The findings show that students’ online access and positive coping strategies could not eradicate their infrastructure and home environment challenges. Lecturers’ learning access equity strategies made learning resources available asynchronously, but having access did not imply that students could effectively self-direct learning. Lecturers designed classroom replication, online practical skills training, online assessment integrity, and student engagement strategies to boost online learning quality, but students who used ineffective online participation strategies had poor engagement. These findings indicate that lecturers and students need to develop more dexterity for adapting and manoeuvring their online strategies across different online teaching and learning modalities. How these online competencies could be developed in higher education are discussed.


Higher education institutions have launched new programmes online for three decades, but their integration of online teaching and learning into on-campus programmes remained less cohesive (Kirkwood & Price, 2014 ). Since early 2020, educational institutions have been shifting online in response to the COVID-19 pandemic. Some consider this kind of emergency remote teaching a temporary online shift during a crisis, whereas online learning involves purposive design for online delivery (Hodges et al., 2020 ). Two years into the pandemic, fully online, blended or hybridised modalities are still being used in response to evolving COVID-19 health advisories (Jaschik, 2021 ). Even though standards for the pedagogical, social, administrative, and technical requirements of online learning have already been published before the pandemic (e.g. Bigatel et al., 2012 ; Goodyear et al., 2001 ), the online competencies of lecturers and students remain critical challenges for higher education institutions during the pandemic (Turnbull et al., 2021 ). Emerging systematic literature reviews about higher education online teaching and learning during the pandemic focus on the clinical aspects of health science programmes (see Dedeilia et al., 2020 ; Hao et al., 2022 ; Papa et al., 2022 ). Understanding the strategies used in other programmes and disciplines is critical for outlining higher education lecturers’ and students’ future online competency needs.

This study, therefore, presents a systematic literature review of the teaching and learning strategies that lecturers and students used to shift online in response to the pandemic and their consequent outcomes. The review was conducted through content analysis and thematic analysis of 36 peer-reviewed articles published from January 2020 to December 2021. It discusses how relevant online competencies for lecturers and students can be further developed in higher education.


A Systematic and Tripartite Approach (STA) (Daniel & Harland, 2017 ) guided the review process. STA draws from systematic review approaches such as the Cochrane Review Methods, widely used in application-based disciplines such as the health sciences (Chandler & Hopewell, 2013 ). It develops systematic reviews through description (providing a summary of the review), synthesis (logically categorising research reviewed based on related ideas, connections and rationales), and critique (providing evidence to support, discard or offer new ideas about the literature).

Framing the review

The following research questions guided the review:

What strategies did higher education lecturers and students use when they shifted teaching and learning online in response to the pandemic?

What were the outcomes arising from these strategies?

Search strategy

Peer-reviewed articles were identified from databases indexing leading educational journals—Educational Database (ProQuest), Education Research Complete (EBSCOhost), ERIC (ProQuest), Scopus, Web of Science (Core Collection), and ProQuest Central. The following search terms were used to locate articles with empirical evidence of lecturers’ and/or students’ shifting online strategies:

(remote OR virtual OR emergency remote OR online OR digital OR eLearning) AND (teaching strateg* OR learning strateg* OR shifting online) AND (higher education OR tertiary OR university OR college) AND (covid*) AND (success OR challenge OR outcome OR effect OR case OR lesson or evidence OR reflection)

The following were the inclusion and exclusion criteria:

Review period—From January 2020 to December 2021, following the first reported case of COVID-19 (WHO, 2020 ).

Language—Only articles published in the English language were included.

Type of article—In order maintain rigour in the findings, only peer-reviewed journal articles and conference proceedings were included, and non-refereed articles and conference proceedings were excluded. Peer-reviewed articles reporting empirical data from the lecturer and/or student perspectives were included. Editorials and literature reviews were examined to deepen conceptual understanding but excluded from the review.

The article’s focus—Articles with adequate descriptions and evaluation of lecturers’ and students’ online teaching and learning strategies undertaken because of health advisories during the COVID-19 pandemic were included. K-12 studies, higher education studies with data gathered prior to January 2020, studies describing general online learning experiences that did not arise from COVID-19, studies describing the functionalities of online learning technologies, studies about tips and tricks for using online tools during COVID-19, studies about the public health impact of COVID-19, or studies purely describing online learning attitudes or successes and challenges during COVID-19 without corresponding descriptions of teaching and learning strategies and their outcomes were excluded.

A list of 547 articles published between January 2020 and December 2021 were extracted using keyword and manual search with a final list of 36 articles selected for review (see Fig.  1 ). The inclusion and exclusion criteria were applied to the PRISMA process (Moher et al., 2009 ). The articles and a summary of coding are found in Appendix .

figure 1

Article screening with the PRISMA process

Data analysis

Content analysis (Weber, 1990 ) and thematic analysis (Braun & Clarke, 2006 ) were used to answer the research questions. Pertinent sections of each article outlining lecturers’ and/or students’ shifting online strategies were identified, read and re-read for data familiarisation. The first author used content analysis to generate eight teaching and learning strategies. These were verified through an inter-rater analysis where a random selection of eight articles was recoded by a second-rater (22.22% of total articles) and confirmed with adequate Cohen’s kappas (Teaching strategies: 0.88, Learning strategies: 0.78). Frequency counts were analysed to answer research question 1.

For the second research question, we first categorised the various shifting online outcomes described in each article and coded each outcome as “success”, “challenge”, or “mixed”. Successful outcomes include favourable descriptions of teaching, learning, or assessment experiences, minimal issues with technology/infrastructure, favourable test scores, or reasonable attendance/course completion rates, whereas challenging outcomes suggest otherwise. Mixed outcomes were not a success or challenge, for example, positive and negative experiences during learning, assessment or with learning infrastructure, or mixed learning outcomes such as positive test scores but lower ratings of professional confidence. Frequency distributions were used to compare the overall successes and challenges of shifting online (see Tables 1 and 2 of “ Findings ” section). Following this, the pertinent outcomes associated with each of the eight shifting online strategies were pinpointed through thematic analysis and critical relationships were visualised as theme maps. These were continually reviewed for internal homogeneity and external heterogeneity (Patton, 1990 ). To ensure trustworthiness and reliability (Creswell, 1998 ), there was frequent debriefing between the authors to refine themes and theme maps, followed by critical peer review with another lecturer specialising in higher education educational technology practices. Throughout this process, an audit trail was maintained to document the evolution of themes. These processes completed the description and synthesis aspects of the systematic literature review prior to critique and discussion (Daniel & Harland, 2017 ).

Descriptive characteristics

Descriptive characteristics of the articles are summarised in Table 1 .

Table 1 shows that articles about shifting online during the pandemic were published steadily between August 2020 and December 2021. About two-thirds of the articles were based on data from the United States of America, Asia, or Australasia, with close to 45% of the articles analysing shifting online strategies used in the disciplines of Natural Sciences and Medical and Health Sciences and around 60% focusing on degree programmes. While there was an exact representation of studies with sample sizes from below 50 to above 150, the majority were descriptive studies, with close to half based on quantitative data gathered through surveys. About half of the articles focused on teaching strategies, while around 40% also examined students' learning strategies. However, only about 20% of the articles had theoretical framing for their teaching strategies. Besides using self-developed theories, the authors also used established theories such as the Community of Inquiry Theory by Garrison et. al. ( 2010 ), the Interaction Framework for Distance Education by Moore ( 1989 ), self-regulated learning by Zimmerman ( 2002 ) and the 5E model of Bybee et. al. ( 2006 ). Different types of shifting online outcomes were reported in the articles. The majority documented the positive and negative experiences associated with synchronous or asynchronous online learning activities, online learning technology and infrastructure, or online assessment. A quarter of the articles reported data on student learning outcomes and attendance/completion rates, while a minority also described teaching workload effects. Table 2 shows other successes and challenges associated with shifting online. Of the articles that examined online learning experiences, over a quarter reported clear successes in terms of positive experiences while about half reported mixed experiences. Majority of the articles examining technology and infrastructure experiences or assessment experiences either reported challenging or mixed experiences. All the articles examining learning outcomes reported apparent successes but only half of those investigating attendance/completion rates found these to be acceptable. Only challenges were reported for teaching workload.

Teaching strategies and outcomes

Lecturers used five teaching strategies to shift online during the pandemic (see Table 3 ).

Online practical skills training

Lecturers had to create online practical skills training . With limited access to clinical, field-based, or laboratory settings, lecturers taught only the conceptual aspects of practical skills through online guest lectures, live skill demonstration sessions, video recordings of field trips, conceptual application exercises, or by substituting skills practice with new theoretical topics (Chan et al., 2020 ; de Luca et al., 2021 ; Dietrich et al., 2020 ; Dodson & Blinn, 2021 ; Garcia-Alberti et al., 2021 ; Gomez et al., 2020 ; Xiao et al., 2020 ). Only in three studies about forest operations, ecology, and nursing was it possible to practice hand skills in alternative locations such as public parks and students’ homes (Dodson & Blinn, 2021 ; Gerhart et al., 2021 ; Palmer et al., 2021 ).

Outcomes : Online practical skills training had different effects on learning experiences, test scores, and attendance/completion rates. Students can attain expected test scores through conceptual learning of practical skills (Garcia-Alberti et al., 2021 ; Gomez et al., 2020 ; Xiao et al., 2020 ). However, not all students had positive learning experiences as some appreciated deeper conceptual learning, but others felt disconnected from peers, anxious about losing hand skills proficiency, and could not maintain class attendance (de Luca et al., 2021 ; Dietrich et al., 2020 ; Gomez et al., 2020 ). Positive learning experiences, reasonable course attendance/completion rates, and higher confidence in content mastery were more achievable when students had opportunities to practice hand skills in alternative locations (Gerhart et al., 2021 ).

Online assessment integrity

Lecturers had to devise strategies to maintain online assessment integrity , primarily through different ways of preventing cheating (see Reedy et al., 2021 ). Pass/Fail grading, reducing examination weightage through a higher emphasis on daily work and class participation, and asking students to make academic integrity declarations were some changes to examination policies (e.g. Ali et al., 2020 ; Dicks et al., 2020 ). Randomising and scrambling questions, administering different versions of examination papers, using proctoring software, open-book examinations, and replacing multiple choice with written questions were other ways of preventing cheating during online examinations (Hall et al., 2021 ; Jaap et al., 2021 ; Reedy et al., 2021 ).

Outcomes : There was concern that shifting to online assessment had detrimental effects on learning outcomes, but several studies reported otherwise (Garcia-Alberti et al., 2021 ; Gomez et al., 2020 ; Hall et al., 2021 ; Jaap et al., 2021 ; Lapitan et al., 2021 ). Nevertheless, there were mixed assessment experiences. When lecturers changed multiple-choice to written critical thinking questions, it made students perceive that examinations have become harder (Garcia-Alberti et al., 2021 ; Khan et al., 2022 ). Some students were anxious about encountering technical problems during online examinations, while others felt less nervous taking examinations at home (Jaap et al., 2021 ). Students also became less confident about the integrity of assessment processes when lecturers failed to set clear rules for open-book examinations (Reedy et al., 2021 ). While Pass/Fail grading alleviated students’ test performance anxiety, some lecturers felt that this lowered academic standards (Dicks et al., 2020 ; Khan et al., 2022 ). More emphasis on daily work alleviated student anxiety as examination weightage was reduced, but students also perceived a corresponding increase in course workload as they had more assignments to complete (e.g. Dietrich et al., 2020 ; Swanson et al., 2021 ).

Classroom replication

Lecturers used classroom replication strategies to foster regularity, primarily through substituting classroom sessions with video conferencing under pre-pandemic timetables (Palmer et al., 2021 ; Simon et al., 2020 ; Zhu et al., 2021 ). Lecturers also annotated their presentation materials and decorated their teaching locations with content-related backdrops to emulate the ‘chalk and talk’ of physical classrooms (e.g. Chan et al., 2020 ; Dietrich et al., 2020 ; Xiao et al., 2020 ).

Outcomes : Regular video conferencing classes helped students to maintain course attendance/completion rates (e.g. Ahmed & Opoku, 2021 ; Garcia-Alberti et al., 2021 ; Gerhart et al., 2021 ). Student engagement improved when lecturers annotated on Powerpoint™ or digital whiteboards during video conferencing (Hew et al., 2020 ). However, screen fatigue commonly affected concentration, and lecturers had challenges assessing social cues effectively, especially when students turned off their cameras (Khan et al., 2022 ; Lapitan et al., 2021 ; Marshalsey & Sclater, 2020 ). Lecturers tried to shorten class duration with asynchronous activities, only to find students failing to complete their assigned tasks (Grimmer et al., 2020 ).

Learning access equity

Lecturers implemented learning access equity strategies so that those without stable network connections or conducive home environments could continue studying (Abou-Khalil et al., 2021 ; Ahmed & Opoku, 2021 ; Dodson & Blinn, 2021 ; Garcia-Alberti et al., 2021 ; Grimmer et al., 2020 ; Kapasia et al., 2020 ; Khan et al., 2022 ; Marshalsey & Sclater, 2020 ; Pagoto et al., 2021 ; Swanson et al., 2021 ; Yeung & Yau, 2021 ). They equalised learning access by making lecture recordings available, using chat to communicate during live classes, and providing supplementary asynchronous activities (e.g. Gerhart et al., 2021 ; Grimmer et al., 2020 ). Some lecturers only delivered lessons asynchronously through pre-recorded lectures and online resources (e.g. de Luca et al., 2021 ; Dietrich et al., 2020 ). In developing countries, lecturers created access opportunities by sending learning materials through both learning management systems and WhatsApp™ (Kapasia et al., 2020 ).

Outcomes : Learning access strategies maintained some level of student equity through asynchronous learning but created challenging student learning experiences. There is evidence that students could achieve expected test scores through asynchronous learning (Garcia-Alberti et al., 2021 ) but maintaining learning consistency was a challenge, especially for freshmen (e.g. Grimmer et al., 2020 ; Khan et al., 2022 ). Some students found it hard to understand difficult concepts without in-person lectures but they also did not actively attend the live question-and-answer sessions organised by lecturers (Ali et al., 2020 ; Dietrich et al., 2020 ; Gomez et al., 2020 ). Poorly designed lecture recordings and unclear online learning instructions from lecturers compounded these problems (Gomez et al., 2020 ; Yeung & Yau, 2021 ).

Student engagement

Lecturers used two kinds of student engagement strategies, one of which was through active learning. Hew et. al. ( 2020 ) fostered active learning through 5E activities (Bybee et al., 2006 ) that encouraged students to Engage, Explore, Explain, Elaborate, and Evaluate. Lapitan et. al. ( 2021 ) implemented active learning through their DLPCA process, where students Discover, Learn and Practice outside of class with content resources and Collaborate in class before Assessment. Chan et. al. ( 2020 ) used their Theory of Change to support active learning through shared meaning-making. Other studies emphasised active learning but did not reference theoretical frameworks (e.g. Martinelli & Zaina, 2021 ). Many described how lecturers used interactive tools such as Nearpod™, and Padlet™, online polling, and breakout room discussions to encourage active learning (e.g. Ali et al., 2020 ; Gomez et al., 2020 ).

Another student engagement strategy was through regular communication and support, where lecturers sent emails, announcements, and reminders to keep students in pace with assignments (e.g. Abou-Khalil et al., 2021 ). Support was also provided through virtual office hours, social media contact after class hours and uploading feedback over shared drives (e.g. Khan et al., 2022 ; Xiao et al., 2020 ).

Outcomes : Among the student engagement strategies, success in test scores tends to be associated with the use of active learning (Garcia-Alberti et al., 2021 ; Gomez et al., 2020 ; Hew et al., 2020 ; Lapitan et al., 2021 ; Lau et al., 2020 ; Xiao et al., 2020 ). On the other hand, positive learning experiences were more often reported when lecturers emphasised care and empathy through their communication (e.g. Chan et al., 2020 ; Conklin & Dikkers, 2021 ). Students felt this more strongly when lecturers used humour, conversational and friendly tone, provided assurance, set clear expectations, exercised flexibility, engaged their feedback to improve online lessons, and responded swiftly to their questions (e.g. Chan et al., 2020 ; Swanson et al., 2021 ). These interactions fostered the social presence of Garrison et. al.’s ( 2010 ) Community of Inquiry Theory (Conklin & Dikkers, 2021 ). However, keeping up with multiple communication channels increased teaching workload, especially when support requests arrived through social media after work hours (Garcia-Alberti et al., 2021 ; Khan et al. 2022 ; Marshalsey & Sclater, 2020 ).

Learning strategies and outcomes

Students used three learning strategies during the pandemic (see Table 4 ).

Online access

Students had to maintain online access , as institutional support for data and technology was rarely reported (Ahmed & Opoku, 2021 ; Laher et al., 2021 ). Students did so by switching to more reliable internet service providers, purchasing more data, borrowing computing equipment, or switching off webcams during class (Kapasia et al., 2020 ; Mahmud & German, 2021 ).

Outcomes : Unstable internet connections, noisy home environments, tight study spaces, and disruptions from family duties were challenges often reported in students’ learning environments (e.g. Castelli & Sarvary, 2021 ; Yeung & Yau, 2021 ). The power supply was unstable in developing countries and students also had limited financial resources to purchase data. To keep studying, these students relied on materials shared through WhatsApp™ groups or Google Drive™ and learnt using mobile phones even though their small screen sizes affected students’ learning quality (Kapasia et al., 2020 ).

Online participation

Students had to maintain online participation by redesigning study routines according to when lecturers posted lecture recordings, identifying personal productive hours, changing work locations at home to improve focus and concentration, and devising study strategies to use online resources effectively, such as through note-taking (e.g. Abou-Khalil et al., 2021 ; Mahmud & German, 2021 ; Marshalsey & Sclater, 2020 ). Students also adjusted their online communication style by taking the initiative to contact lecturers through email, discussion forums, or chat for support, and learning new etiquette for video conferencing (Abou-Khalil et al., 2021 ; Dietrich et al., 2020 ; Mahmud & German, 2021 ; Simon et al., 2020 ; Yeung & Yau, 2021 ). Students recognised the need for active online participation (Yeung & Yau, 2021 ) but most tended to switch off webcams and avoided speaking up during class (Ahmed & Opoku, 2021 ; Castelli & Sarvary, 2021 ; Dietrich et al., 2020 ; Khan et al., 2022 ; Lapitan et al., 2021 ; Marshalsey & Sclater, 2020 ; Munoz et al., 2021 ; Rajab & Soheib, 2021 ).

Outcomes : Mahmud and German ( 2021 ) found that students lack the confidence to plan their study strategies, seek help, and manage time. Students also lacked confidence and switched off webcams out of privacy concerns or because they felt self-conscious about their appearances and home environments (Marshalsey & Sclater, 2020 ; Rajab & Soheib, 2021 ). Too many turned off webcams and this became a group norm (Castelli & Sarvary, 2021 ). Classes eventually became dominated by more vocal students, making the quieter ones feel left out (Dietrich et al., 2020 ).

Positive coping

Students’ positive coping strategies included family support, rationalising their situation, focusing on their future, self-motivation, and making virtual social connections with classmates (Ando, 2021 ; Laher et al., 2021 ; Mahmud & German, 2021 ; Reedy et al., 2021 ; Simon et al., 2020 ).

Outcomes : Positive coping strategies helped students to improve learning experiences, maintain attendance/completion rates, and avoid academic integrity violations during online examinations (Ando, 2021 ; Reedy et al., 2021 ; Simon et al., 2020 ). However, these strategies cannot circumvent technology and infrastructure challenges (Mahmud & German, 2021 ), while the realities of economic, family, and health pressures during the pandemic threatened their educational continuity and caused some to manifest negative coping behaviours such as despondency and overeating (Laher et al., 2021 ).

Higher education online competencies

This systematic review outlined eight teaching and learning strategies for shifting online during the pandemic. Online teaching competency frameworks published before the pandemic advocate active learning, social interaction, and prompt feedback as critical indicators of online teaching quality (e.g. Bigatel et al., 2012 ; Crews et al., 2015 ). The findings suggest that lecturers’ student engagement strategies aligned with these standards, but they also needed to adjust practical skills training, assessment, learning access channels, and classroom teaching strategies. Students’ online participation and positive coping strategies reflected how online learners could effectively manage routines, schedules and their sense of isolation (Roper, 2007 ). Since most students had no choice over online learning during the pandemic (Dodson & Blinn, 2021 ), those lacking personal motivation or adequate infrastructure had to develop online participation and online access strategies to cope with the situation.

The eight teaching and learning strategies effectively maintained test scores and attendance/completion rates, but many challenges surfaced during teaching, learning, and assessment. Turnbull et. al. ( 2021 ) attribute lecturers’ and students’ pandemic challenges to online competency gaps, particularly in digital literacy or competencies for accessing information, analysing data, and communicating with technology (Blayone et al., 2018 ). However, the study findings show that digital literacy may not be enough for students to overcome infrastructure and home environment challenges in their learning environment. Lecturers can try helping students mitigate these challenges by providing asynchronous resource access through access equity strategies. Yet, students may not successfully learn asynchronously unless they can effectively self-direct learning. Lecturers may have pedagogical knowledge to create engaging active online learning experiences. How these strategies effectively counteract students’ inhibitions to turn on webcams and speak up during class remains challenging. Lectures may also have the skills to set up different online communication channels, but students may not actively engage if care and empathy are perceived to be lacking. Furthermore, lecturers’ online assessment strategies may not always balance academic integrity with test validity.

These findings show that online competencies are not just standardised technical or pedagogical skills (e.g. Goodyear et al., 2001 ) but “socially situated” (Alvarez et al., 2009 , p. 322) abilities for manoeuvring strategies according to situation and context (Hatano & Inagaki, 1986 ). It encompasses “dexterity” or finesse with skill performance (Merriam-Webster, n.d.). The pandemic demands one to be “flexible and adaptable” (Ally, 2019 , p. 312) amidst shifting national, institutional and learning contexts. Online dexterity is needed in several areas. Online learning during the pandemic is rarely unimodal. Establishing the appropriate synchronous-asynchronous blend is a critical pedagogical decision for lecturers. They need dexterity across learning modalities to create the “right” blend in different student, content, and technological contexts (Baran et al., 2013 ; Martin et al., 2019 ). Lecturers also need domain-related dexterity to preserve authentic learning experiences while converting subject content online (Fayer, 2014 ). Especially when teaching skill-based content under different social distancing requirements, competencies to maintain learning authenticity through simulations, alternative locations, or equipment may be critical (e.g. Schirmel, 2021 ). Dexterity with online assessment is also essential. Besides preventing cheating, lecturers need to ensure that online assessments retain test validity, improve learning processes and are effective for performance evaluation (AERA, 2014 ; Sadler & Reimann, 2018 ). Another area is the dexterity to engage in online communication that appropriately manifests care and empathy (Baran et al., 2013 ). Since online teaching increases lecturers’ workload (Watermeyer et al., 2021 ), dexterity to balance student care and self-care without compromising learning quality is also crucial.

Access to conducive learning environments critically affects students’ online learning success (Kapasia et al., 2020 ). While some infrastructure challenges cannot be prevented, students should have the dexterity to mitigate their effects. For example, when disconnected from class because of bandwidth fluctuations, students should be able to find alternative ways of catching up with the lecturer rather than remaining passive and frustrated (Ezra et al., 2021 ). Self-direction is critical during online learning because it is the ability to set learning goals, self-manage learning processes, self-monitor, self-motivate, and adjust learning strategies (Garrison, 1997 ). Students need the dexterity to manage self-direction processes across different courses, learning modalities, and learning schedules. Dexterity to create an active learning presence through using appropriate learning etiquette and optimising the affordances of text, audio, video, and shared documents during class is also essential. This can support students' cognitive, social, and emotional engagement across synchronous and asynchronous modalities, individually or in groups (Zilvinskis et al., 2017 ).

Future directions

Online learning is highly diverse and increasingly dynamic, making it challenging to cover all published work for review. In this study, we have analysed pandemic-related teaching and learning strategies and their outcomes but recognise that a third of the studies were from the United States and close to half from natural or health science programmes. The findings cannot fully elucidate the strategies implemented in unrepresented countries or disciplines. Recognising these limitations, we propose the following as future directions for higher education:

Validate post-pandemic relevance of online teaching and learning strategies

The eight strategies can be validated through longitudinal empirical studies, theoretical analyses or meta-synthesis of literature to establish their relevance for post-pandemic teaching and learning. Studies outside the United States and the natural and health science disciplines are especially needed. This could address the paucity of theoretical framing in the articles reviewed, even with theories developed before the pandemic (e.g. Garrison et al., 2010 ; Moore, 1989 ; Zimmerman, 2002 ).

Demarcate post-pandemic online competencies

The plethora of descriptive studies in the articles reviewed is inadequate for understanding the online competencies driving lecturers’ pedagogical decision-making and students’ learning processes. In situ studies adopting qualitative methods such as grounded theory or phenomenology can better demarcate lecturers’ and students’ competencies for “why and under which conditions certain methods have to be used, or new methods have to be devised” (Bohle Carbonell et al., 2014 , p. 15). A longitudinal comparison of these studies can provide a better understanding of relevant post-pandemic competencies.

Develop dexterity with respect to application of online competencies

Higher education institutions use technology workshops, mentoring, and instructional consultation to develop competencies in technology-enhanced learning (e.g. Baran, 2016 ). However, dexterity to manoeuvre contextual differences may be better fostered through exploration, discovery, and exposure to varied contexts of practice (Mylopoulos et al., 2018 ). Innovative ways of developing dexterity with respect to how online competencies can be applied and the efficacy of these methodologies are areas for further research.

The COVID-19 pandemic has significantly increased the adoption and utilisation of online learning. While the present review findings suggest that the strategies lecturers and students employed to shift online during the pandemic have contributed to maintaining educational continuity and test scores but many outstanding issues remained unresolved. These include failure for students to gain an enhanced learning experience, problems encountered in designing and implementing robust assessment and online examinations, cases of academic misconduct, inequitable access to digital technologies, and increased faculty workload. Lecturers and institutions need to tackle these issues to fully leverage the opportunities afforded by online teaching and learning. Further, our findings revealed that the level of online dexterity for both students and teachers need to be enhanced. Therefore, higher education institutions must understand and develop online dexterity institutional frameworks to ensure that pedagogical innovation through online learning can be continually sustained, both during the pandemic and beyond.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

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Teaching strategies

Learning strategies




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Students' perceptions, educational challenges and hope during the COVID‐19 pandemic

Crystal i. bryce.

1 School of Medicine, University of Texas at Tyler, Tyler Texas, USA

Ashley M. Fraser

2 School of Family Life, Brigham Young University, Provo Utah, USA

Associated Data

The data that support the findings of this study are available from the corresponding author, upon reasonable request.


The COVID‐19 pandemic has disrupted the lives of US students both at home and at school. Little is known regarding how adolescents perceive COVID‐19 has impacted (both positively and negatively) their academic and social lives and how protective factors, such as hope, may assist with resilience. Importantly, not all pandemic experiences are necessarily negative, and positive perceptions, as well as potential protective factors, are key to understanding the pandemic's role in students' lives.

Utilizing quantitative and qualitative approaches, the present study descriptively examined 726 6th through 12th grade (51% female, 53% White) students' perceptions of how COVID‐19 related to educational and life disruptions, and positive aspects of their lives, within the United States. Analyses additionally explored the role of pre‐pandemic hope in improving feelings of school connectedness during the pandemic.

Results showed that most students felt that switching to online learning had been difficult and their education had suffered at least moderately, with a sizeable proportion of students feeling less academic motivation compared with last year. When asked to share qualitative answers regarding perceived challenges and positive aspects of life, themes were consistent with quantitative perceptions. Students' pre‐pandemic hope positively predicted students' feelings of school connectedness.


Findings paint a complex picture of youth's COVID‐19 experiences and have implications for proactive ways to support students as COVID‐19 continues to affect daily life and educational structures and practices.


Although researchers have noted a marked increase in adolescents' mental health issues, little is known regarding how adolescents feel their lives have been specifically impacted by COVID‐19, how protective factors such as hope may assist with resilience and if there are any positive experiences that may be a by‐product of the pandemic (Dvorsky et al.,  2020 ; Jones et al.,  2021 ). The COVID‐19 pandemic has altered the lives of US students both at home and school, particularly as students have had to attend school virtually, potentially disrupting feelings of school connectedness. Outside of school, students may face increased concern and stress regarding COVID‐19's potential impact on health, friendships and the future (Magson et al.,  2021 ). Importantly, not all pandemic experiences may have been perceived as negative, and perceptions may have differed across school level given that students are at different stages of educational and personal development in middle versus high school. Therefore, the goal of the present study was to examine middle school (MS) and high school (HS) students' perceptions, both positive and negative, surrounding the impact of COVID‐19 on their academic and personal lives and to understand if hope was a potential protective factor in promoting students' school connectedness during this historic event.

Given that the existing literature on adolescents during the COVID‐19 pandemic has primarily focused on negative experience and situations, such as the social, academic and recreational repercussions of social isolation, increased stress and detrimental mental health implications (de Figueiredo et al.,  2021 ; Ravens‐Sieberer et al.,  2021 ; Samji et al.,  2021 ), the present study extends this literature by highlighting the importance of positive experiences and skills both regarding life in general and school. Specifically, one such positive skill is hope, a cognitive, motivational construct, which may serve as a protective factor in supporting feelings of connection with school (Marques et al.,  2017 ); this is important as less school connectedness may be tied to changes in the school format and negatively affect academic motivation, which would have implications for student outcomes.


Developmentally, adolescence is characterized by identity development, increased autonomy and educational preparation that help determine life course trajectories (Kroger,  2004 ). This period of developmental growth makes adolescents especially vulnerable to the stress of a global pandemic (Tottenham & Galván,  2016 ; Zhou,  2020 ). The repercussions of the pandemic have been pervasive, with disruptions occurring across contexts including homes and schools. Yet, little is known regarding how MS and HS adolescents describe and rate their pandemic experiences. Exploring individual perceptions is key to understanding how adolescents make meaning of their own experiences and perceptions (Grusec & Goodnow,  1994 ; Witherspoon & Hughes, 2014 ), particularly when those perceptions surround disruption in education and their personal life.

2.1. Educational disruptions

Nearly 93% of households with school‐age children reported some form of distance learning during the COVID‐19 pandemic (Mcelrath, 2020 ). School closures and online learning requirements necessitated significant changes for students, with face‐to‐face interaction with peers and teachers suddenly changing to online‐only interfacing. The sudden, pervasive disruptions may have yet‐to‐be‐known implications for students' outcomes (Golberstein et al.,  2020 ). However, as recent studies have shown, it is likely that adolescents' motivation to participate and engage were impacted as schools were shuttered (Klootwijk et al.,  2021 ; Maiya et al.,  2021 ). Although researchers and educational leaders have posited negative educational impacts, and the circumstances certainly warrant conjecture, few studies have asked students how they feel their motivation has changed and whether their education has suffered, despite students' reports of decreased academic motivation and school connection (Klootwijk et al.,  2021 ; Maiya et al.,  2021 ).

2.2. Life disruptions

In addition to educational disruption, daily life also changed drastically for many students during the pandemic, with social gatherings, extracurricular activities, and daily happenings being severely limited. Additionally, there were likely drastic changes within the home environment, with students remaining at home for schooling, caregivers/parents working remotely and potentially increased time with the entire household unit being isolated at home, all of which may have been either a positive or negative experience for the adolescent depending on family dynamics. Persistent media coverage of the health risks of COVID‐19 may have also led to concern over personal and family health as well as society's future as a whole. Researchers have qualitatively explored adolescents' COVID‐19 experiences, and themes showed that challenges covered a wide spectrum, but adolescents overwhelmingly reported negative outcomes including loneliness, mental health problems and academic struggle (Scott et al.,  2021 ). To build on this knowledge, the present study aims to descriptively explore how adolescents described concern over their social lives, personal and family health and society 1 year into the pandemic.

Further, although there has been emphasis on how the pandemic has negatively affected students, it is also important to understand if there were factors that supported students during this time (Dvorsky et al.,  2020 ). For example, quantitative research conducted among adolescents in Hong Kong showed that even when faced with school closures and pandemic‐related concerns, a sizeable proportion of students reported stronger relationships with their peers and parents (Zhu et al.,  2021 ). New hobbies may have also been taken on during the pandemic. These supportive aspects of adolescents' lives may have served as protective factors that encouraged them to remain positive during this difficult time. Indeed, adolescents who reported having strong support from friends and family during the pandemic showed less academic concern (Christ & Gray,  2022 ) and more academic motivation (Klootwijk et al.,  2021 ) than those who reported low levels of support. The aforementioned studies only focused on survey measures that asked students to quantitatively rate positive aspects of their lives and social support, with an emphasis on relationships. To fully understand the implications of these positive experience during the pandemic, it is important to allow adolescents to share their own experiences qualitatively, which may provide more comprehensive information on different ways that adolescents viewed aspects of their lives as positive, beyond interactions with others. In addition to these pandemic‐induced experiences, there may have been protective factors in students' pre‐pandemic lives that helped to support them connect with school; one such protective factor may have been hope.


Hope is a malleable, cognitive‐motivational construct that encompasses individuals' goal pursuit (Fraser et al.,  2022 ). Hope theory posits that hopeful individuals are able to demonstrate behaviours and have beliefs that are directly related to and align with goal pursuit (Snyder,  2002 ). Specifically, high‐hope individuals are able to identify their future goals, determine how to reach those goals and reroute when necessary(i.e. pathways thinking) and feel confident in their pursuit of goals (i.e. agency thinking). Furthermore, hope is an additive and iterative process in that students set goals, work towards and reach those goals, which, in turn, helps them feel motivated to set and pursue new goals. Within an academic setting, students with high hope can identify academic goals, recognize the steps necessary to achieve those goals and/or tenable alternatives and feel confident in their approach to reaching those goals; successful completion of these steps not only bolsters students' hope but also their desire to confidently continue educational pursuits (Marques et al.,  2017 ). In terms of academic outcomes, extant research has shown that hope positively predicts achievement (Marques et al.,  2017 ), academic effort (Levi et al., 2014 ) and students' connection to academics (Snyder,  2002 ; Van Ryzin,  2011 ) and that a positive association exists between hope and school connectedness (Dixson & Scalcucci, 2021 ; Liu et al.,  2020 ; You et al.,  2008 ). Further, hope serves as a protective factor during tumultuous times. For example, researchers have shown that high hope reduced symptoms related to post‐traumatic stress disorder among hurricane survivors (Glass et al.,  2009 ) and children who experienced rocket attacks (Kasler et al.,  2008 ).

In terms of the COVID‐19 pandemic, hope has been shown to operate as a protective factor in numerous ways, particularly in improving coping approaches and mental health. Among American adults surveyed during the pandemic, high hope was associated with decreased stress surrounding COVID‐19 and positively associated with well‐being and emotional control, a potential mechanism for coping in stressful situations (Gallagher et al.,  2021 ). Hope was also shown to support adults' abilities to demonstrate resilience when faced with difficult situations and was associated with better overall psychological health (Yıldırım & Arslan,  2020 ). The two aforementioned studies highlight how hope may operate as a protective factor for individuals' well‐being. Academically, among college students, hope positively predicted students' ability to overcome trauma/distress associated with the pandemic (Hu et al.,  2021 ), which highlights how being able to think about future goals and pathways to success can serve as a protective factor against pandemic distress. Importantly, to our knowledge, researchers have yet to examine how adolescents' pre‐pandemic hope may have acted as a supportive factor for youth outcomes.

When considering the role of hope during the pandemic for adolescents, high‐hope students may be more able to identify ways to remain engaged in online settings or modified learning structures and feel efficacious in pursing their planned routes, thus supporting students' feelings of connection to school. Despite theoretical support, to our knowledge, researchers have not yet examined the role of hope for middle and high school students' outcomes during the pandemic. As society begins to navigate new and changing mitigation strategies and approaches, and schools return to in‐person learning, it is critical that we understand how strengths‐based skills such as hope may serve as protective factors.


In the midst of a global pandemic, more information is needed regarding students' own perceptions of how COVID‐19 impacted their lives. Furthermore, students' pre‐pandemic hope may be an important protective factor in supporting students' school connectedness. The present study addressed two main research questions. First, how did MS and HS students quantitatively rate their feelings about educational and life disruptions as related to COVID‐19, and what did students qualitatively report as the largest perceived challenges and positive aspects of their lives during Spring 2021? Although descriptive in nature, we hypothesized that students would report that COVID had a large impact on their academic experience and life more generally. Second, did 2020 hope (pre‐pandemic) serve as a protective factor for students' 2021 school connectedness? We hypothesized that 2020 hope would positively predict 2021 school connectedness.

5.1. Participants and procedure

In 2021, all students ( M age  = 14.52 SD age  = 1.94) in 6th through 12th grade were recruited from two schools ( MS n  = 394; HS n  = 332; total N  = 726; 59% response rate) that drew from rural and suburban areas in the Southwestern United States; 51% of students were female, 13% qualified for special education services, and 61% qualified for free/reduced lunch. In terms of race/ethnicity, 53% identified as White, 40% as Hispanic/Latinx, 4% as Black, and 3% as other. Within this district, during the 2020/2021 school year, the schools were closed (remote learning) from August 2020 through mid‐October 2020. Learning shifted to in‐person classes from mid‐October 2020 through December 2020. Then, schools were closed again (remote learning) from January 2021 through late February 2021. At the time of data collection (mid‐February 2021), the state in which the schools were located had a daily COVID‐19 infection rate of 246 new cases per 100 000, and a COVID‐19 death rate of 12.2 per 100 000 (White House COVID‐19 Team JCC,  2021 ).

District data collection targeted enrolled students, who completed the online survey during the school day. Surveys were conducted in February 2020 (prior to the COVID‐19 pandemic, students in‐person learning) and February 2021(during pandemic, remote learning). Teachers provided the survey link and oriented students to the survey, and students were informed they could skip questions or choose not to participate. All students in 5th to 12th grades were invited to complete the survey each year and entered an anonymous (to the researchers) identifier that allowed data to be matched from year to year. For the purpose of this study, we utilized data from MS and HS students who were in 6th through 12th grade during the 2021 data collection (5th through 11th in 2020). This approach allowed for the use of 2020 survey data as a predictor of 2021 survey data. A university institutional review board approved the study.

Participants were included in the present study if they had data in 2021. Educational disruption due to COVID‐19, life concerns due to COVID‐19, perceived challenges and positive aspects of life and school connectedness were collected in February 2021; hope was the only 2020 construct included in the study. Demographic data for 2021 were available for all students within the district in the focal grades ( n  = 1241). Students in the analytic sample differed from those who did not complete the survey in terms of gender ( χ 2 (1) = 13.19, P  < .01) and special education designation ( χ 2 (1) = 12.41, P  < .01), but not in terms of race/ethnicity ( χ 2 (3) = 2.64, P  = .62) or free/reduced lunch qualification ( χ 2 (1) = 1.17, P  = .28).

Of the analytic sample ( n  = 726), 347 (48%) participants were missing 2020 data. Of those 347 students, 130 (37%) were new to the district in 2021 (MS n  = 75; HS n  = 55), which would account for their missingness on 2020 data. When comparing all students missing 2020 data ( n  = 347) with those without missing data, there was no significant difference between missing data groups for MS students' 2021 school connectedness. For HS students, a significant difference emerged, with those who were missing 2020 hope data reporting lower 2021 school connectedness ( M  = 2.37, SD  = 0.62) than those with 2020 hope data ( M  = 2.62, SD  = 0.67), F (1, 329) = 11.47, P  < .01. We ran additional analyses to examine those who had missing data but were enrolled at the district in 2020 ( n  = 217), compared with those who had 2020 data. Similar to the other missing data analyses, there were no significant differences between missing‐data groups for MS students. For HS students, the difference on school connectedness became less significant (marginal) when comparing students who had the opportunity to take the 2020 survey but did not ( M  = 2.43, SD  = .61) with those who had completed the 2020 survey ( M  = 2.63, SD  = 0.67), F (1, 274) = 3.92, P  = .05. Lastly, we examined if the two groups without 2020 data (those who were new to the district and those who were not) differed on school connectedness. For both MS and HS students, there were no significant differences in mean‐level school connectedness between the two groups, F (1, 229) = 2.32, P  = .13 and F (1, 113) = 1.40, P  = .24, respectively.


5.2.1. educational disruptions due to covid‐19.

Students completed three items about how COVID‐19 had caused disruption to their education (Magson et al.,  2021 ). Two items focused on changes due to COVID‐19 (i.e. ‘How difficult has it been to switch to online learning?’ and ‘Do you think your education is suffering due to disruption from COVID‐19?’) and were rated on a 5‐point Likert‐type scale ( 1 = not at all to 5 = extremely) . One item that focused on motivation (i.e. ‘Compared to how motivated you were last school year to do school work, how motivated do you feel NOW to do your school work?’) was answered on a 3‐point scale ( 1 = much less motivated, 2 = about the same, 3 = much more motivated ). These items were chosen because they have been used in previous work among adolescents during COVID‐19 (Magson et al.,  2021 ).

5.2.2. Life concerns due to COVID‐19

Students reported their level of COVID‐19 related stress ( 1 = not at all stressful to 5 = very stressful ) on five questions that addressed concerns about not seeing friends, not attending social events, contracting COVID‐19 themselves, friends/family contracting COVID‐19 and thinking about the future of our society. Researchers have used this scale in previous work focusing on COVID‐19 and demonstrated high reliability (Cronbach's alpha = 0.91) (Magson et al.,  2021 ). The present study utilized item‐level data, and we chose this approach and measure to allow for a more nuanced understanding of students' feelings across specific areas.

5.2.3. Perceived challenges and positive aspects of life

Utilizing open‐ended response questions, students were asked, ‘What is the biggest challenge in your life right now?’ and ‘What is the best thing in your life right now?’ These items were chosen to provide students with the opportunity to express their own feelings about perceived challenges and positive aspects of their lives.

5.2.4. Hope

Students reported their feelings of hope (6‐point Likert‐type scale, 1 = none of the time to 6 = all of the time) using the 6‐item Children's Hope Scale (Snyder et al.,  1997 ). We chose this scale given its alignment with hope theory. Additionally, this scale has had good reliability in previous work (Valle et al.,  2004 ) and the present study (Cronbach's alphas = 0.87 and 0.89, MS and HS, respectively). A higher mean composite indicated more hope.

5.2.5. School connectedness

Students reported their feelings of school connectedness (Mancini et al.,  2003 ) on three items (e.g. ‘I feel like I matter in my school’) using a 4‐point Likert‐type scale ( 1 = strongly disagree to 4 = strongly agree ). We chose this scale because it captures student perceptions of their belongingness and importance at school. This scale showed good reliability in the present study (Cronbach's alpha = 0.70 and 0.72, MS and HS, respectively) and previous work (Meléndez Guevara et al.,  2021 ). A higher mean composite indicated more school connectedness.

5.2.6. Gender

Given that previous research has shown inconsistent results regarding gender differences on individuals' responses in the face of serious national/international events (i.e. natural disasters, other pandemics; Kronenberg et al.,  2010 ; Sprang & Silman,  2013 ), we included gender as a covariate in study analyses. Student gender (0 = female, 1 = male) was obtained via school records and included as a covariate in study analyses.

5.3. Analytic plan

We first examined descriptive statistics and Pearson correlations for MS and HS students' feelings about educational and life disruptions as related to COVID‐19. We additionally examined school‐level differences on motivation using a chi‐square test, conducted analysis of covariance (ANCOVA) for the remaining two educational variables and ran a multivariate analysis of covariance (MANCOVA) for the five life questions, controlling for gender for the ANCOVAs and MANCOVA. We next coded each open‐ended response to find the common themes regarding students' perceived challenges and positive aspects of life; overall, most students provided one to two‐word responses for the open‐ended questions, allowing for the responses to be easily grouped by theme. The first author coded the themes, and any that were more complex were coded for multiple themes. All items that were coded for multiple themes were then reviewed by the second author. This approach allowed us to examine if students' open‐ended responses confirmed or expanded upon the responses for the survey measures.

Lastly, we conducted a structural equation model (SEM) to examine if 2020 hope predicted 2021 school connectedness. Model fit was examined; meeting at least three of the following criteria were used to determine acceptable model fit given: χ 2 P ‐value > .05, the comparative fit index (CFI) ≥ .95, the standardized root mean square residual (SRMR) ≤ .08 and root mean square error of approximation (RMSEA) ≤ .06 (Hu & Bentler,  1999 ). Descriptive statistics, correlations, ANCOVAs and MANCOVA were conducted in SPSS 25. The SEM models were conducted in Mplus 8.

Descriptive statistics are presented for all study variables by school level in Table  1 , and correlations among study variables are presented in Table  2 . Hope and school connectedness were positively, significantly correlated for both MS and HS students. As expected, across school level, all education concerns related to COVID‐19 were significantly positively correlated, as were all correlations among the life distress questions related to COVID‐19.

Descriptive statistics for all study variables

MinimumMaximumMeanSDGroup comparison statisticsComparison statistics 95% CI for mean
Hope (2020)1.50/ 6.00/ 4.28/ 1.12/
Agency subscale1.33/ 6.00/ 4.28/ 1.26/
Pathways subscale1.33/ 6.00/ 4.28/ 1.18/
School Connectedness (2021)1.00/ 4.00/ 2.62/ 0.71/
Education concerns related to COVID‐19 (2021)ANCOVA statistics
Difficulty switching to online learning1.00/ 5.00/ 3.31/ 1.47/ (1, 722) = .61,  = .43[3.17, 3.45]/
Education is suffering due to disruption1.00/ 5.00/ 3.31/ 1.53/ (1, 722) = .01,  = .97 [3.16, 3.46]/
Current school motivation compared with last year1.00/ 3.00/ 1.79/ 0.72/
Life distress related to COVID‐19 (2021)MANCOVA statistics (5, 703) = 4.09,  < .01, Wilks' Λ = .97
Not seeing friends1.00/ 5.00/ 2.98/ 1.54/ (1, 707) = 11.18,  < .01 [2.82, 3.11]/
Not attending social events1.00/ 5.00/ 2.54/ 1.53/ (1, 707) = 1.91,  = .17[2.40, 2.69]/[
Catching COVID‐191.00/ 5.00/ 2.82/ 1.65/ (1, 707) = 10.01,  < .01[2.66, 2.98]/[
Friends/family catching COVID‐191.00/ 5.00/ 3.48/ 1.52/ (1, 707) = 10.29,  < .01 [3.33, 3.63]/
Thinking about future of society1.00/ 5.00/ 3.13/ 1.48/ (1, 707) = 1.78,  = .18 [2.99, 3.28]/

Note : Middle school presented first followed by high school in bold .

Abbreviation: CI, confidence interval.

Pearson correlations among study variables

1. Hope
2. School connectedness.29 /
3. Difficulty switching to online learning−.08/− −.02/
4. Education is suffering due to disruption−.14/− −.08/− .66 /
5. Not seeing friends−.07/− −.01/ .32 / .23 /
6. Not attending social events−.05/− .10 .33 / .24 / .58 /
7. Catching COVID‐19.12/ .15 / .10 / .18 / .21 / .26 /
8. Friends/family catching COVID‐19.12/ .13 / .16 / .15 / .30 / .33 / .68 /
9. Thinking about future of society.10/ .07/ .26 / .28 / .33 / .25 / .32 / .46 /

Notes : Middle school results are presented before high school results in bold . #3–4 are education concerns related to COVID‐19 (2021); #5–7 are life distress questions related to COVID‐19 (2021). The item asking about students' motivation compared with last year was not included given the ordinal nature of the responses. For the hope measure, the agency and pathways subscales were significantly correlated at .69 and .73 for middle and high school, respectively.

6.1. Perceptions of educational disruptions and life as related to COVID‐19

6.1.1. educational disruptions related to covid‐19.

Approximately half of all students (in both MS and HS) felt that the switch to online learning had been moderately or extremely difficult and that their education had suffered moderately to extremely (Figure  1 ). There were no mean‐level differences by school level on either question (see Table  1 for statistics).

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Students' perceptions of educational disruptions due to COVID‐19

When asked how motivated students were to do work now compared with last school year, 39% of MS students reported being much less motivated, 43% had about the same motivation, and 18% were much more motivated. Among HS students, 45% reported being much less motivated, 36% had about the same motivation, and 19% were much more motivated. The chi‐square test of motivation level differences by school level was not significant, χ 2 (2) = 3.82, P  = 0.15.

6.1.2. Life concerns related to COVID‐19

Of note, over 50% of MS students reported thinking about their friends/family members contracting COVD‐19 as either quite or very stressful. Around 44% of MS and 38% of HS students felt that thinking about the future of society was quite or very stressful. Figures  2 and ​ and3 3 show frequencies of responses to each item by school level. A MANCOVA showed that there were school‐level differences in life concerns related to COVID‐19 (see Table  1 for statistics).

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Students' concern about contracting COVID‐19. HS, high school; MS, middle school

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Students' concern about life experiences as related to COVID‐19. HS, high school; MS, middle school

6.1.3. Students' perceived challenges and positive aspects of life during COVID‐19

Students' open‐ended responses to ‘the biggest challenge’ and ‘the best thing’ in their lives were categorized and counted. In both MS and HS, the top three challenges described were school (45%, 40%), COVID‐19 (7%, 4%) and the future (4%, 6%). Both MS and HS students also reported not having any challenges (4% for each school level). See Table  3 for exemplar quotes for each category.

Examples of qualitative responses to for top themes

‘Online school and group projects because they make us work with people and no one talks so I have to do the whole thing alone’. MS student
‘My biggest challenge is being in remote learning and really try to take in what is being taught to me for me to remember in the long run’. HS student
‘My biggest challenge is trying to get all my school work done because the teachers leave a lot of work and it is very stressful’. HS student
‘School is the biggest challenge I face I use to have A's and B's when we were in person now it is too much for me’. MS student
‘Not being able to have the face to face one on one time with the teachers to help if I do not understand something’. HS student
‘The biggest challenge in my life right now is staying motivated to do school because I honestly lost all motivation’. HS student
‘Not being able to fully put my attention in the lesson because its not the same as in person’. HS student
‘COVID and not being able to do certain things that I want’. HS student
‘The biggest challenge in my life right now is thinking my family will catch covid’. MS student
‘The biggest challenge in my life right now is trying to stay safe from Covid’. MS student
‘My challenge in life is hoping me or any friends or family do not get the coronavirus’. MS student
‘My biggest challenge in life right now is covid‐19 and how uncertain it makes me feel’. HS student
‘Thinking about and preparing for the future of our nation and society as a whole’. HS student
‘Thinking about the future’. MS student
‘Just figuring out what comes after high school’. HS student
‘The best thing my life at the moment are having my family and friends to talk to when time get stressful’. MS student
‘The best things in my life right now is the love and support I get from my family and friends’. MS student
‘My friends even tho we cannot hang out everyday like we used to they always got my back and they are like sisters to me I do not know what to do without them’. MS student
‘The best thing in my life is being able to stay home with my family the entire day instead o only a couple hours each day’. MS student
‘The best thing in my life right now is my family. I am grateful that I am able to spend time with them and I am glad that they are still here with me. It's reassuring and calming. They helped me every step along the way and they guide me when they know that I am doing something wrong. They let me talk about little problems and understand me’. HS student
‘The best thing in my life right now is spending more time with my parents because I hardly spent time with them’. HS student
‘The best thing in my life right now is that I'm doing alright in school and that I'm understanding much better’. MS student
‘Getting to have good grades more than last year’. MS student
‘My drive to be successful in school’. HS student
‘The best thing in my life is that I've been able to keep up good grades throughout the whole year so far, and I'm honestly so proud of myself for it’. HS student
‘Still being able to do school’. HS student
‘Playing the guitar’. MS student
‘Going to the gym’. HS student
‘The best thing in my life right now is getting back into sports’. HS student
‘Skateboarding and dirt bike riding’. MS student
‘Video games’. HS student

Notes : MS = middle school; HS = high school. Responses were lightly edited for capitalization and punctuation.

In MS and HS, the Top 3 positive aspects of their lives were interpersonal relationship/interactions (44%, 41%), school (8%, 6%) and hobbies (5%, 8%). See Table  2 for exemplar quotes for each category. Interestingly, 30 students (3.5% of the entire sample) reported ‘nothing’ as being the best thing in their lives.

6.2. Pre‐pandemic hope predicting 2021 school connectedness

To examine if 2020 hope predicted students' 2021 school connectedness, we conducted SEM models, separate by school level, where 2020 hope and 2021 school connectedness were latent variables and gender was a covariate. The measurement models with the latent variables showed good fit (MS: χ 2 (26) = 37.17, P  = .07; RMSEA = .03; RMSEA 90% confidence interval = 0.00, 0.06; CFI = .98; SRMR = .05; HS: χ 2 (26) = 46.47, P  = .01; RMSEA = .05; RMSEA 90% confidence interval = 0.03, 0.07; CFI = .97; SRMR = .04). All items significantly loaded onto their respective constructs and the standardized loadings ranged from 0.48 to 0.84 for the MS model and from 0.53 to 0.84 for the HS model. In both models, only a single item (the same item in both models) fell below 0.64, which loaded onto the school connection variable. We retained that item for theoretical reasons and given the limited number of items to measure school connectedness.

When examining the structural model for MS students, the model fit was good, χ 2 (34) = 48.22, P  = .05; RMSEA = .03; RMSEA 90% confidence interval = 0.00, 0.05; CFI = .97; SRMR = .06. Hope (2020) positively significantly predicted 2021 school connectedness ( B  = .30, P  < .01; R 2   =  0.15). For HS students, the structural model fit was good, χ 2 (34) = 58.44, P  = .01; RMSEA = .05; RMSEA 90% confidence interval = 0.03, 0.07; CFI = .97; SRMR = .04). Hope (2020) positively significantly predicted 2021 school connectedness ( B  = .42, P  < .01; R 2   =  0.27). Gender, which was included as a covariate was not significant in either model. See Figure  4 for all parameter estimates.

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Structural equation model of latent hope predicting latent school connectedness. * P  < .01. Middle school parameter estimates are presented first, followed by high school parameter estimates in bold. Unstandardized parameter estimates are followed by standardized parameter estimates in parentheses.


The present study examined students' perceptions about education and life 1 year into the COVID‐19 pandemic. Overall, results showed that most students felt that switching to online learning had been difficult and their education had suffered at least moderately, with a sizeable proportion of students feeling less motivated compared with last year. Even still, students' 2020 hope positively predicted students' feelings of school connectedness. Interestingly, in terms of life concerns related to COVID‐19, some of the patterns that emerged appeared to be different across school level, with MS students reporting higher levels of concern than HS students in a number of areas; yet, when asked to share qualitative answers regarding perceived challenges and positive aspects of life, the same themes emerged across school level. Findings highlight students' own perceptions with implications for support as societal restrictions are lifted.

7.1. Life concerns, disruptions and positive aspects

Our first research question attempted to understand middle and high school students' perceptions of the severity of the pandemic's influence on multiple aspects of life including school, health and social lives. Students' quantitative responses to life disruptions related to COVID‐19 were mostly consistent with the themes that emerged in their qualitative responses regarding their ‘biggest current challenge’. The Top 3 qualitative challenges reported were school, COVID‐19 and the future. Given the serious changes to schooling during the pandemic, it is not surprising that issues surrounding school were cited as a challenge for a large proportion of students. This finding aligns with research conducted in May 2020 where students were asked to qualitatively report on their biggest COVID‐19 challenges and 23.7% of adolescents reported challenges related to academics (Scott et al.,  2021 ). It may be that more students in the present study (40%–45%) reported challenges related to school because they had been immersed in virtual schooling for an extended period, which highlights the felt pressure of online/remote schooling with time. Although the initial switch to online learning may have been an acute shock, it could be that persisting conditions have led to even more fatigue than the initial transition.

The next most often reported qualitative concern was COVID‐19 (generally); the quantitative data showed that over half of students felt that concerns about their family/friends contracting COVID‐19 was quite or very high, whereas their concern for themselves contracting COVID‐19 was relatively low or not stressful. This finding aligns with other research that showed 43% of HS students were very concerned about COVID‐19 in general and students expressed more concern that someone they know would become infected than themselves (Ellis et al.,  2020 ). This finding could reflect adolescents' awareness that older and medically compromised individuals are more prone than teens to have severe complications when contracting COVID‐19.

The third most often reported qualitative theme regarded students' futures. The proportion of responses (4%–5%) citing concerns for the future in the present study aligned with extant work (Scott et al.,  2021 ). It is interesting that there was consistency in percentage of students responding this way as compared with previous work, even though the data in the present study were collected much later in the pandemic. It could be that COVID‐19 vaccination efforts played a role in how students responded. At the time of data collection (February 2021), vaccine administration was increasing, with an emphasis on steps to ‘return to normal’. Further, there was some disconnect in terms of how stressful students quantitatively reported thinking about the future of society as related to COVID‐19, with a sizeable portion of students reporting it was quite or very stressful (44% MS, 37% HS) when responding to the quantitative questions. It could be that when asked to arrive at their own conclusions about challenges, thinking about the future was not necessarily at the forefront of their minds, especially given the consistent, every day focus on schooling. However, it is important to acknowledge that quantitative responses showed students reported thinking about the future as very stressful and high levels of stress can have negative implications for student's well‐being both psychologically and academically (Burkhart et al.,  2017 ; Repetti et al.,  1999 ; Sisk & Gee,  2022 ).

Importantly, school‐level differences emerged on three of the quantitative life concerns (i.e. not seeing friends, catching COVID‐19 and friends/family catching COVID‐19), with MS students expressing more concern than HS students. Previous research has shown that, in general, adolescents' stress tends to decrease with age (Seiffge‐Krenke et al.,  2009 ). It may be that this pattern of gradual decrease is reflected in the MS students' reporting higher stress levels than the HS students. Additionally, as youth enter later adolescence, they begin to utilize more advanced coping skills, such as seeking others for support and employing more cognitively complex thought processes (e.g. imaging solutions and outcomes) surrounding problems (Seiffge‐Krenke et al.,  2009 ). The aforementioned approaches may have helped the older adolescents manage their stress surrounding COVID‐19. The school‐level differences on stress items within the present study highlight the importance of potentially targeting MS students for intervention to help them cultivate coping skills as COVID‐19 continues to be present.

7.1.1. Positive aspects

It is critical to understand not only how students have struggled during the pandemic but also what helped them thrive, especially if those positive aspects of their lives could be further nurtured and/or used as sources of support. Students overwhelmingly mentioned their relationships and interactions with others as being the best aspects of their lives during the pandemic, followed by school and hobbies. This finding highlights the importance of social connections even during times of physical social distancing (Orben et al.,  2020 ) and aligns with research showing the importance of peer interactions during adolescence. Research published focusing on COVID‐19 and adolescent outcomes has shown that strong social ties have been critical in supporting youth's psychological well‐being during the pandemic (Bernasco et al.,  2021 ; Hutchinson et al.,  2021 ; Parent et al.,  2021 ), likely because these interactions help relieve stress surrounding many aforementioned areas of concern. Both school and hobbies may be positive aspects of students' lives as they help provide a sense of normalcy/routine. Indeed, with so much disruption overall, a daily academic and extracurricular schedule may help youth feel grounded and secure despite high levels of uncertainty in other areas. During times of high stress, such as a pandemic, adults can help support youth in identifying and engaging with the positive aspects of their lives to reduce stress and promote well‐being (Ronen et al.,  2014 ). Our findings support consistent daily activities and social support as some such positive aspects, even if circumstances call for these activities to look different (e.g. observing social distancing, connecting online, etc.).

7.2. Hope and school connectedness

For both MS and HS students, pre‐pandemic hope positively predicted feelings of school connectedness during the pandemic. This finding aligns with extant research during non‐pandemic times (You et al.,  2008 ), and the consistency seems of particular relevance, given the rapid shift to remote learning. It is possible that hope promotes school connection because students with high hope are able to identify their goals and recognize how school can support their goal attainment (Fraser et al.,  2022 ). In turn, they may feel a stronger connection because of their ability to identify the relevance and importance of school for them. Practically, hope may be an important cognitive‐motivational skill to support students' re‐entry onto campuses after, potentially, more than a year of remote learning.

Indeed, previous research has shown that hope is a skill that can be taught and cultivated during adolescence, a developmental period in which youth set increasingly complex goals and make autonomous choices (McDermott & Hastings,  2000 ; Snyder et al.,  2002 ). In light of the pandemic, adult socializers (e.g. teachers, school counsellors and parents) can help adolescents learn how to set meaningful goals and remind youth to use their past goal pursuit experience as a way to take appropriate steps, be it changing the approach or utilizing the same approach, to promote future goal pursuit (Snyder et al.,  2002 ). In this way, adults can help the student learn that hope is iterative and that the student can draw upon their lived experience during the pandemic in a way that will support their future goals. Further, adults can be an important social support for students by reminding them that there are numerous paths to goal completion and that obstacles (such as those related to the pandemic) can be overcome, especially when students are feeling defeated or less sure of themselves. School interventions utilizing the aforementioned approaches have been successfully implemented during this developmental period (McDermott & Hastings,  2000 ). Taken together, in the context of the COVID‐19 pandemic, hope may help students reinvest in and reacclimate to in‐person learning by helping them identify new goals, plan routes to goal attainment and feel efficacious in that attainment. Parents and educators can actively assist in this process.

Not surprisingly, students shared feelings of decreased motivation, moderate to extreme concerns about difficulty switching to an online modality and that their education had suffered due to COVID‐19. Our findings are consistent with other research on youth concerns during the pandemic (Zaccoletti et al.,  2020 ). These findings are particularly interesting juxtaposed with the finding that hope supported school connectedness. Indeed, hope may be an important protective factor moving forward to help students plan ways to overcome educational disruptions, pandemic‐related or otherwise, and to support and promote motivation, particularly as students begin to return to in‐person learning.

7.3. Limitations and future direction

The findings from the present study provide a much‐needed insight into students' perceptions and concerns surrounding the pandemic, and the potential for hope to help them in the transition back to school; however, it is not without limitation. We only included a single school district, which may decrease generalizability; specifically, our sample precluded us from examining patterns across different geographic locations, or differing remote‐schooling structures, that may have differentially been affected by the COVID‐19 pandemic. More research is necessary that investigates perceptions among students at various ages from multiple districts. Furthermore, although we conducted an analysis with estimators robust to missing data for the SEM model, and believe that the missing data were missing at random, we are unable to ascertain why there was a mean‐level difference in school connectedness between those with and without hope scores. Future studies should attempt to replicate these findings with less missing data. Students' responses to the open‐ended questions were grouped into broad categories, as the majority of students only included one‐ to two‐word answers; this limited our ability to pursue more in‐depth analyses. Future work should focus on asking questions that provoke more elaborate responses to better understand the intricacies of students' perceptions.

Additionally, the present study did not have access to information regarding family demographics (e.g. socio‐economic status, living situation, social mobility and neighbourhood data) or parent–child relationships. During the pandemic, it is possible that students' experiences were different across family‐level demographic data, particularly with regard to caregiver involvement at home for remote schooling, necessary work outside of the home and students having to take on additional responsibilities due to the pandemic and their caregivers' schedules. More research is necessary that incorporates these contextual factors. Lastly, as it was not within the scope of the study, future research is needed that examines how students' hope, school connectedness, life concerns and educational disruptions may have manifested different by varying demographic groups (e.g. urbanicity and race/ethnicity).

As students continue to return to classrooms, it is important that researchers, educators and caregivers understand how students' perceptions regarding their lives and educational disruptions have manifested during a year of online schooling and the pandemic and the potential role that hope can play as a protective factor. Fostering students' hope may help them connect with school and readjust to in‐person learning more quickly. Furthermore, because many students reported declines in their motivation during online school, supporting students' goals and hope skills may help increase their school motivation. In general, the knowledge gained regarding students' concerns and positive experiences from the present study present an early look at how the COVID‐19 pandemic has affected students and may serve as a starting point when examining long‐term effects of the pandemic on youth in the future.

Bryce, C. I. , & Fraser, A. M. (2022). Students' perceptions, educational challenges and hope during the COVID‐19 pandemic . Child: Care, Health and Development , 1–13. 10.1111/cch.13036 [ PMC free article ] [ PubMed ] [ CrossRef ]

This research did not receive any specific grant from funding agencies in the public, commercial or not‐for‐profit sectors.


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Identifying the Leadership Challenges of K-12 Public Schools During COVID-19 Disruption: A Systematic Literature Review


  • 1 Faculty of Education, Southwest University, Chongqing, China.
  • 2 Faculty of Education, Umm Al-Qura University, Makkah, Saudi Arabia.
  • 3 University of Applied Science and Technology, Khuzestan, Ahvaz, Iran.
  • 4 School of Foreign Languages, Yulin University, Shaanxi, China.
  • PMID: 35432078
  • PMCID: PMC9009316
  • DOI: 10.3389/fpsyg.2022.875646

Globally, the COVID-19 pandemic is triggering a public health emergency and crisis on a large scale, with far-reaching effects and severe damage to all aspects of politics, economy, cultural and social life, and health. Consecutive outbreaks over the past nearly 2 years of "living with COVID-19" have forced most schools to physically close, resulting in the largest educational disruption in human history. In turbulent times of the COVID-19 crisis, school leaders are facing numerous major challenges germane to school governance and leadership. The key objective of the study is to fully explore the prospective challenges principals are encountering in public schools in times of COVID-19. To fulfill the research purpose, a systematic literature review (SLR) was carried out to investigate the leadership challenges. As a result, a total of 24 challenges were explored through SLR approach. Frequency analysis approach was initially applied to figure out the most significant challenges. Accordingly, seven challenges were found statistically significant as showing frequency ≥ 50 each. Irrevocably, the study works as a contribution to K-12 school leadership by providing guidance for current and future leaders in crisis based on practical investigation, experiences, and recommendations. Policy makers can leverage these findings to make necessary adjustments to school policy to better prepare school leaders for crisis. Additionally, the findings of the current study are believed to have profound implications for future research. These findings expand our current understanding on school leadership in time of crisis that needs further investigation. Subsequent studies can quantitatively and/or qualitatively validate these leadership challenges findings regarding a particular school context.

Keywords: COVID-19 pandemic; educational crises; leadership challenges; school leaders; systematic literature review.

Copyright © 2022 Parveen, Tran, Alghamdi, Namaziandost, Aslam and Xiaowei.

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Conflict of interest statement

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.

Steps of Systematic Literature Review.

Selection of primary studies through…

Selection of primary studies through PRISMA guidelines.

Frequency analysis of identified challenges.

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The impact of school closures during the covid-19 pandemic on reading fluency among second grade students: socioeconomic and gender perspectives.

Shelley Shaul

  • Edmond J. Safra Brain Research Center for the Studies of Learning Disabilities, Department of Learning Disabilities, University of Haifa, Haifa, Israel

Introduction: The acquisition of reading skills is a crucial milestone in early education, with formal instruction and practice playing pivotal roles. The outbreak of COVID-19 led to widespread school closures and a shift to remote learning.

Methods: This study aimed to investigate the effects of school closures on reading acquisition and fluency among a large sample of second-grade children, considering socioeconomic status (SES) and gender differences. In 2019, a cohort of 2228 second-grade students from 34 schools was assessed for word reading fluency and comprehension. In 2020, during the pandemic, 765 students from a subsample of 20 original schools were re-evaluated using the same measures. The study also collected school-related data.

Results: The findings from the entire sample indicated no significant differences in fluency and comprehension scores between children in the second grade in 2019 and 2020. However, a significant interaction emerged when analyzing low SES versus high SES children. Children from low SES backgrounds exhibited notably lower reading scores after a year of remote learning due to the COVID-19 outbreak. Moreover, the disparity in reading scores between low SES and high SES children nearly doubled in 2020. Gender differences were also detected.

Discussion: These results underscore the impact of remote learning during the COVID-19 crisis on exacerbating gaps in reading fluency and comprehension between children from high and low SES backgrounds. The implications of these findings highlight the critical role of in-person schooling and targeted support for disadvantaged students, especially during pivotal stages of reading development.

1 Introduction

The global outbreak of COVID-19 in early 2020 prompted widespread school closures across many countries, including Israel, resulting in a significant shift toward remote learning ( Kuhfeld et al., 2020 ; Lake and Dusseault, 2020 ; United Nations, 2020 ). This unprecedented situation led to changes in the educational landscape, with students adapting to shortened school days delivered through technological platforms ( Hall et al., 2020 ; Kuhfeld et al., 2020 ). Moreover, parents took on a more prominent role in delivering the curriculum in many instances ( Reimer et al., 2021 ).

A fundamental milestone in early elementary education is the acquisition of reading skills. The process of learning to read involves substantial formal instruction and practice ( Stanovich and West, 1989 ). However, the adverse impact of COVID-19 on reading acquisition was particularly pronounced among disadvantaged children who faced unequal access to educational resources ( UNESCO, 2020 ). This disparity is a significant concern, particularly with studies highlighting potential “Matthew Effect” dynamics during the pandemic, where existing gaps in reading ability between children from different socioeconomic backgrounds could be further exacerbated ( García-Muiña et al., 2021 ). The “Matthew Effect” concept underscores how initial advantages can magnify disparities over time ( Stanovich, 1986 ), which, in the context of reading, could suggest that children from lower socioeconomic status backgrounds might fall behind even more in their reading development. Furthermore, parents of elementary school children reported a reduction in learning-related activities during COVID-19 closures ( Andrew et al., 2020 ), potentially compounding challenges for struggling readers.

The second grade is a pivotal stage where children transition from decoding-based reading strategies to more fluent and accurate reading ( Chall, 1983 ; Bar-Kochva, 2013 ). Although studies on the impact of COVID-19 closures on reading have emerged, many have focused on later stages of elementary school (from 3rd grade onwards; Kuhfeld et al., 2020 ; Engzell et al., 2021 ; Kaffenberger, 2021 ; Relyea et al., 2023 ). Few large-scale studies have addressed the effects of COVID-19 school closures on reading development during earlier foundational stages ( Ardington et al., 2021 ). The Israeli Ministry of Education’s expert panel highlighted the need to investigate and comprehend gaps arising from COVID-19, particularly in early childhood, and emphasized the importance of empirical studies based on validated tools conducted at multiple time points ( Kesner Baruch et al., 2021 ).

This study aims to address a gap in the literature by examining reading acquisition among a substantial sample of Hebrew-speaking second-grade children—an age group that has received less attention during the early elementary years. Specifically, we investigate the trajectory of fluency development among children of diverse socioeconomic backgrounds over a year, encompassing both pre-COVID-19 conditions and the subsequent year, within the same district.

1.1 Reading fluency development

Reading fluency is a critical skill characterized by the ability to read with automaticity, speed, accuracy, proper expression, and appropriate phrasing ( National Reading Panel (US), 2000 ). As reading fluency advances, the cognitive load associated with decoding decreases, allowing more cognitive resources to be allocated to comprehending the text’s meaning ( Wolf and Katzir-Cohen, 2001 ; Perfetti, 2007 ; Stevens et al., 2017 ). The progression of oral reading fluency typically takes place between the second and third grades, persistently evolving throughout the elementary years ( Chall, 1983 ). Early elementary oral reading fluency contributes to proficient silent reading, which becomes crucial in later elementary school ( Price et al., 2016 ). Numerous studies across diverse languages underscore the significance of reading fluency, revealing its predictive role in reading comprehension, the ultimate goal of reading ( Klauda and Guthrie, 2008 ; Kim et al., 2010 ; Stevens et al., 2017 ; Nevo et al., 2020 ).

Assessing reading fluency frequently involves measuring the accurate pronunciation of words within a restricted timeframe. For instance, the Test of Word Reading Efficiency (TOWRE) evaluates the ability to pronounce printed words both accurately and fluently, reflecting the comprehension of the read words ( Torgeson et al., 1999 ; Fuchs et al., 2001 ; Good et al., 2001 ). Proficient automatic sight-word reading is fundamental for fluid and natural text comprehension ( Miller and Schwanenflugel, 2008 ; Kuhn et al., 2010 ). Thus, tests gaging the number of correctly read words within a given duration serve as valuable tools for identifying potential reading difficulties ( Valencia et al., 2010 ). Research underscores that during early grades, reading fluency significantly contributes to comprehension, a principle that is particularly pronounced in second-grade readers ( Fuchs et al., 2001 ; Valencia et al., 2010 ). Reading in context demands the activation of semantics, as readers simultaneously process words while aiming to extract textual meaning ( Katzir et al., 2006 ). Consequently, the amalgamation of syntactic rules and semantic structures is essential for constructing cohesive units of ideas. Insufficient automation at lower processing levels (letters or words) could impede processing at higher levels (sentences or texts; Logan, 1997 ).

This study’s focus is on Hebrew-speaking children, with Hebrew characterized as an Abjad writing system. An Abjad writing system predominantly consists of consonantal representation with sporadic and incomplete vowel representation ( Eviatar and Share, 2013 ). Hebrew is available in two forms: pointed (shallow orthography) and unpointed (deep orthography). Early reading acquisition in first grade revolves around shallow pointed Hebrew, allowing for rapid association between letters and sounds due to comprehensive phonological cues ( Share and Levin, 1999 ; Shany et al., 2012 ). As such, most children become skilled decoders by the end of first grade, heightening the importance of speed and fluency ( Lipka et al., 2016 ). The progression to partially pointed texts, particularly in second and third grades, exposes readers to lexico-morpho-orthographic knowledge utilization ( Shany et al., 2012 ).

In nurturing reading fluency in first and second graders, the recommendation is for students to engage in daily reading aloud and silent practice, utilizing materials tailored to their level of competence ( National Reading Panel (US), 2000 ; The Israeli Ministry of Education, 2014 ). The shift to remote instruction is believed to have potentially hindered teachers’ ability to facilitate ample reading fluency practice opportunities.

1.2 The challenges of remotely teaching literacy to diverse learners

The abrupt shift to remote learning during the pandemic posed significant challenges for educators, particularly in teaching literacy to young children. These learners, who had not yet become independent readers, faced obstacles in navigating technological tools independently ( Sucena et al., 2022 ). As literacy development heavily relies on face-to-face interaction, the transition to remote learning presented hurdles in providing the necessary constant feedback and personalized attention required for learning to read and write ( Relyea et al., 2023 ).

Teachers were thrust into an unfamiliar landscape, requiring them to adapt and innovate in the realm of online instruction with limited prior experience. This shift was especially arduous for educators in the early elementary grades ( Giovannella et al., 2020 ; Kruszewska et al., 2020 ; Letzel et al., 2020 ; Dotan et al., 2021 ). A study in Israel conducted by Dotan et al. (2021) among first- and second-grade teachers revealed their struggles in remote teaching, including challenges in fostering reading fluency and comprehension, addressing the needs of struggling readers, and assessing literacy skills remotely. Beyond curriculum adaptation, teachers also encountered difficulties in teaching diverse learners. Notably, the digital divide was exacerbated by socioeconomic status (SES) disparities, with 75% of low-SES school teachers reporting unequal access to computers among their students, compared to 46% in middle-high SES schools ( Dotan et al., 2021 ).

Despite the hurdles, some positive outcomes were observed due to school closures. The increased involvement of parents in providing home support during remote learning potentially contributed to emotional and academic advancements ( Immerfall, 2020 ). Nonetheless, the prevailing sentiment from research indicates learning loss resulting from school absences ( Kuhfeld et al., 2020 ; Engzell et al., 2021 ; OECD, 2023 ).

In evaluating the pandemic’s impact on learning, the term “unfinished learning” becomes relevant—a concept encompassing missed instruction due to school closures ( Lambert and Sassone, 2020 ; The National Authority for Measurement and Evaluation in Education, 2023 ). Notably, this term does not imply a permanent deficit; instead, with proper support, students can attain the necessary mastery.

Additionally, the term “vulnerable children” takes on significance in this context, especially concerning children from low SES backgrounds. Their vulnerability extends to economic hardships, limited access to resources, reduced support, and heightened stress at home ( Drane et al., 2020 ; Masters et al., 2020 ). The literature review reinforces the imperative to attend to these vulnerable learners, particularly those from low SES backgrounds who are at risk of accumulating academic gaps, especially in reading, during the COVID-19 period ( Kaffenberger, 2021 ; Relyea et al., 2023 ).

In conclusion, the challenges of remotely teaching literacy to diverse learners during the pandemic were multifaceted. Teachers navigated the complexities of adapting to online instruction, while students faced barriers in receiving the personalized attention necessary for literacy development. The unequal access to technology further exacerbated disparities, with vulnerable learners from low SES backgrounds at greater risk of falling behind. Despite the potential benefits of home support, learning loss remained a prevalent concern. The educational community’s focus on addressing these challenges is essential for fostering equitable learning outcomes and supporting vulnerable children’s academic growth.

Several studies have attempted to estimate the extent of learning gaps resulting from school closures, drawing insights from previous instances of learning loss during periods like summer vacations or crises. Bao et al. (2020) predicted that kindergarten children in the United States would experience an average loss of 31% in their reading ability gained in 2020. Kuhfeld et al. (2020) expanded on this by demonstrating that third- to seventh-grade students could lose around 35% of their reading gains during the COVID-19 period compared to a typical school year. Furthermore, the impact was more pronounced among students with low socioeconomic status (SES). In their predictions about school achievement variability during the pandemic, they estimated a reading score decrease of 1.2 times lower than typical year scores ( Kuhfeld et al., 2020 ). Hevia et al. (2022) examined 10-15-year-old readers and indicated that the younger readers, as well as those with low SES, showed the greatest learning loss in reading during the COVID-19 pandemic.

An interesting recent meta-analysis review ( Betthäuser et al., 2023 ) identified 42 studies from 15 countries on learning progress among primary and secondary school children during the COVID-19 pandemic. It was found that students experienced a loss of approximately 35% of a school year’s learning. On average, the learning advancement of school-aged children was significantly reduced during the pandemic. Furthermore, the review implies that the pandemic has intensified educational disparities among children from diverse SES, which have been found before the pandemic.

This trend receives support from research on regular periods, such as the summer vacation, during which the learning loss of children from low socioeconomic backgrounds is significantly more substantial than that of those from moderate to high socioeconomic backgrounds (e.g., Burkam et al., 2004 ; Downey et al., 2004 ; Kim and White, 2008 ; Allington et al., 2010 ).

A simulation study conducted across seven low- and middle-income countries by Kaffenberger (2021) projected that a school closure lasting one-third of a regular year during third grade could lead to a year-long loss in learning until tenth grade, disproportionately affecting students in lower-income countries.

These trends have been found not only in reading but also in mathematical abilities, Blaskó et al. (2022) sought to assess the potential impact of pandemic-related learning losses in mathematics across 22 European countries, surveying 4,400 4th graders. Their study was based on data from an international achievement survey conducted before the pandemic, namely the Trends in International Mathematics and Science Study 2019. The findings revealed significant disparities among European countries regarding the availability of essential distance-learning resources, parental backgrounds, and school differences. These discrepancies in country standings are likely attributed to both the affluence of and inequalities within the respective countries, which, in turn, can impact the effect of learning loss.

A recent study conducted in the US by Relyea et al. (2023) found that the average reading achievement gain during the 2020–2021 school year was lower compared to the 2018–2019 school year. The observed effect sizes for learning loss were 0.54, 0.27, and 0.28 standard deviations for grades 3, 4, and 5, respectively. Similar gaps in reading skills were detected among second-grade students in South Africa ( Ardington et al., 2021 ). This study compared reading skills of students assessed before (2019) and during the pandemic (2020), revealing a reading gap ranging from 57 to 70% for English-speaking second graders.

A study focused on fifth-grade students in Germany, employing real-time assessments through a reading comprehension task in 2020 after school closures, highlighted a learning loss of 11–17% compared to previous measurements ( Schult et al., 2022 ).

A recent systematic review ( Panagouli et al., 2021 ), synthesizing data from 42 studies primarily conducted in Europe, Asia, and America, investigates the impact of online learning and modified educational methods on school-aged students during the COVID-19 pandemic. The review encompasses students aged 8 to 22 and revealed varied effects: The most prominent trend indicated that students experienced learning loss, especially in math and reading, though some benefited. Younger students and those with neurodevelopmental disorders or special education needs faced greater challenges. Additionally, parents reported similar trends, observing declines in their children’s performance, though some noted benefits from online learning. Teachers mainly reported academic gaps, particularly in mathematics and reading. Despite challenges, younger students showed enthusiasm for interactive learning materials, suggesting their positive effects should be considered.

Furthermore, a meta-analysis of 18 studies ( König and Frey, 2022 ) mainly from the United States and Europe (predominantly Germany and the Netherlands), assessed the impact of COVID-19-related school closures on student achievement. The analysis showed a negative effect, with a weekly learning loss of −0.022. It also tentatively suggested that younger primary school students were more adversely affected compared to older students, possibly due to their lower self-regulated learning capabilities and the vital role of teacher scaffolding in regular instruction. The analysis suggested that remote learning was more effective in later lockdown phases than initially, possibly due to the familiarity gained with established online learning apps.

A study spanning from third to ninth grade in Switzerland investigated the impact of COVID-19-related school closures and the effectiveness of in-person versus distance learning in math and language ( Tomasik et al., 2021 ). It was found that while older students could somewhat offset the effects of school closures, younger students faced significant challenges. Learning progress for younger children not only slowed down, potentially affecting future development, but also became more varied. While a small group of primary school students benefited from closures, others experienced severe declines in performance. These children are at risk of falling behind academically, emphasizing the importance of addressing their needs.

These studies collectively underscore the pervasive impact of COVID-19-induced school closures on students’ reading skills, transcending socioeconomic, cultural, and linguistic boundaries. Overall, these findings emphasize that the pandemic’s repercussions on reading development have been particularly detrimental for children from low-SES backgrounds. Consequently, students returned to school with substantial and divergent learning gaps, necessitating targeted efforts from educators to address and mitigate these disparities. Notably, learning losses were more pronounced among students from less educated and low SES households ( Engzell et al., 2021 ; Kaffenberger, 2021 ; Betthäuser et al., 2023 ; Relyea et al., 2023 ).

1.3 Reading and gender

Gender constitutes another significant contextual factor within the realm of children’s reading development. Despite standardized literacy instruction in classrooms, disparities in reading achievement between boys and girls have been consistently observed. Numerous studies have consistently highlighted noteworthy gender differences in reading achievement across the entire spectrum of reading abilities within educational settings ( Chatterji, 2006 ; Mullis et al., 2007 ; Logan and Johnston, 2010 ; Robinson and Lubienski, 2011 ; Reardon et al., 2019 ).

Remarkably, girls consistently outperform boys in reading achievement ( Chatterji, 2006 ; Mullis et al., 2007 ; Logan and Johnston, 2010 ; Robinson and Lubienski, 2011 ; Katzir et al., 2018 ; Reardon et al., 2019 ), and these gender differences do not display a marked declining trend across elementary or secondary schooling ( Reardon et al., 2019 ; Reilly et al., 2019 ). Additionally, substantial gender imbalances exist in poor reading, with boys being disproportionately represented ( Reilly et al., 2019 ). Notably, prior empirical evidence ( Coles and Hall, 2002 ; Mullis et al., 2007 ) consistently indicates that girls report higher reading frequency compared to boys. Gender-linked disparities in reading frequency may indeed influence variations in reading performance.

Support for gender differences can be found in the latest PISA report, in which girls outperformed boys in reading by an average of 24 points across OECD countries, indicating a universal gender gap. Among low performers, boys outnumbered girls, constituting 31% compared to 22% in reading proficiency. Conversely, among top performers, girls slightly outnumbered boys, with 8% versus 6% on average across OECD nations. In Israel, ranked 30th out of 81 countries, girls achieved a mean reading score of 486, surpassing boys by 24 points (462). While girls’ literacy achievements declined compared to previous years, boys showed improvement. Despite this narrowing trend, the gender gap still favors girls in reading proficiency. The gender gap scenario in Israel closely mirrors the OECD average. The Israeli Ministry of Education emphasized, based on the PISA 2022 findings, that the gender gaps in reading proficiency translate to nearly a year of schooling.

While gender effects in remote learning have primarily been explored among older students, limited research has delved into gender-specific effects on young learners during the COVID-19 pandemic. Some studies suggest that females tend to exhibit greater adaptability to collaborative and technology-based instruction, while others find that males often display a higher comfort level with the technical aspects of remote learning platforms ( Jones et al., 2021 ).

It is vital to underscore that most existing studies have focused on older children rather than those in the early stages of elementary school, where reading acquisition begins. As such, this present study emphasizes reading acquisition among second-grade students, aiming to bridge a gap in the literature pertaining to reading development during COVID-19. This research particularly targets children from diverse backgrounds at this pivotal stage. Furthermore, the study’s focus extends to examining whether gender-related differences manifest differently among boys and girls.

Research Questions:

1. What is the effect of COVID-19 on second-grade children’s reading fluency, and is there an interaction between COVID-19, SES, and gender on reading fluency?

2. What is the effect of COVID-19 on second-grade children’s comprehension fluency, and is there an interaction between COVID-19, SES, and gender on comprehension fluency?

2.1 Participants

The study included primary school students from the Israeli public education system, all Hebrew speaking children with typical IQs, encompassing various socioeconomic status (SES) backgrounds in the southern region of Israel. The participants’ age range was between seven and 8 years old, with a relatively equal distribution of boys (49%) and girls (51%). None of the children in the sample exhibited significant neurological difficulties. The division of children into SES groups was based on the Ministry of Education’s scoring system for schools, utilizing neighborhood and parental demographic information including education and income. A total of 20 schools were examined at both time points with 5% of the schools representing high SES, 55% medium SES and 40% of the schools from low SES. A comprehensive overview of sociodemographic characteristics is presented in Table 1 .

Table 1 . Sociodemographic characteristics of the sample.

2.2 Measures

2.2.1 reading fluency.

Word reading fluency was assessed using the TOWRE test ( Katzir et al., 2012 , based on Torgeson et al., 1999 ). Administered individually, participants were tasked with orally reading 80 single words as swiftly and accurately as possible within a 45-s timeframe. The words were progressively ordered in terms of complexity. Scores were computed based on the number of correct words read in 45 s and the error percentage. The internal consistency reliability (α) of this assessment was 0.95.

2.2.2 Comprehension fluency

A group-administered task was employed to evaluate semantic comprehension fluency ( Yinon and Shaul, 2017 , based on Hutzler and Wimmer, 2004 ). This task consisted of 21 sentences spanning a range of everyday topics. Participants were required to read each sentence and promptly indicate whether it was semantically accurate or erroneous, all within a two-minute timeframe. The scores were calculated based on the number of accurately marked sentences within 2 min and the error percentage. The internal consistency reliability (α) for this task was 0.93.

2.3 Procedure

The necessary approvals were secured from the Ministry of Education and the relevant university’s ethics committee prior to data collection. All assessments were individually administered to participants in a designated quiet room within the school premises. Each assessment session lasted approximately 10 min. During the initial year of the study (October 2019), 1,460 children from 20 schools underwent testing. In the subsequent year (October 2020), 815 children were tested from the same 20 schools. All assessments were conducted individually during school hours in a controlled environment.

3.1 First research question: the effect of COVID-19, SES and gender on reading fluency

To answer the first research question regarding the combined effect of COVID-19, SES, and gender on reading fluency, a univariate analysis of covariance (ANCOVA) was run with COVID-19, SES, and gender as independent variables, reading fluency as the dependent variable, and school as a covariate variable. The descriptive statistics of the word reading fluency is presented in Table 2 . The analysis revealed no main effect of COVID-19 or gender, F’s < 1. The main effect of SES was significant, F (2, 1988) = 39.15, p  < 0.001, η 2  = 0.04, indicating that participants in the Low SES schools ( m  = 21.75, SE = 0.45) had lower reading fluency compared to medium SES ( m  = 25.67, SD = 0.35; p  < 0.001) which were lower than the High SES ( m  = 31.64, SE = 1.32; p  < 0.05). There were significant differences between all the different SES in reading fluency ( p  < 0.001).

Table 2 . Mean and (SD) of word reading fluency in the among the different SES groups and gender in both years of the study.

The interaction between COVID-19 and SES was significant, F(2, 1988) = 3.99, p  < 0.05, η 2  = 0.01. Post-hoc analyses revealed that the negative effect of COVID-19 existed only in low SES schools, F (1, 761) = 6.89, p  < 0.01, η 2  = 0.01. Low SES Participants in year 2 (post-COVID-19) had lower reading fluency ( m  = 20.56, SD = 0.64) than year 1 participants (pre-COVID-19; m  = 22.66, SD = 0.47). There was no effect of COVID-19 on medium SES, F (1, 1,371) = 2.14, p  = 0.14, nor High SES ( F  < 1). See Figure 1 .

Figure 1 . Word-reading fluency among the different SES levels in both years.

In addition, the interaction between COVID-19 and gender was significant, F (1, 1988) = 3.82, p  = 0.05, η 2  = 0.00. Post-hoc analyses revealed a marginally significant effect of gender on reading fluency in year 1, in year 1, F (1, 1,455) = 3.36, p  = 0.07, η 2  = 0.00, indicating that females’ reading fluency ( m  = 24.04) was slightly lower than that of males’ ( m  = 25.08, SD = 10.48). In year 2, there the performance of females was higher than the males.

The interaction between SES and gender, as well as the triple interaction between COVID-19, SES, and gender, were insignificant (F’s < 1).

Following this ANCOVA analysis, another ANCOVA analysis was run without school as a covariate variable. This analysis yielded similar trends: a significant main effect of SES, F (2, 1989) = 24.54, p  < 0.001, η 2  = 0.02, and interaction of COVID-19 and SES, F(2, 1989) = 3.99, p < 0.01, η 2  = 0.01; a marginally significant interaction between COVID-19 and gender, F (1, 1989) = 3.82, p  = 0.05, η 2  = 0.00; and the insignificant effects were the main effects of gender and COVID-19, and the interactions of SES × COVID-19, and SES × COVID-19 × gender (all F’s < 1).

3.2 Second research question: the effect of COVID-19, SES and gender comprehension fluency

To address the second research question concerning the combined impact of COVID-19, SES, and gender on comprehension fluency, two similar univariate analyses of covariance (ANCOVAs) were conducted with COVID-19, SES, and gender as independent variables, comprehension fluency as the dependent variable, and with and without school as a covariate variable. The descriptive statistics of the reading comprehension fluency is presented in Table 3 . The analysis that included school as a covariate variable revealed a significant main effect of SES, F (2, 1958) = 14.46, p  < 0.001, η 2  = 0.02, indicating that participants in low SES schools ( m  = 5.82, SE = 0.17) had lower reading fluency compared to medium SES ( m  = 7.00, SD = 0.13; p  < 0.001) and high SES ( m  = 7.21 SD = 0.51). There were no differences in comprehension fluency between high SES and medium ( p  = 0.66) ( Figure 2 ). This analysis did not indicate main effects of COVID-19, F (1, 1958) = 2.58, p  = 0.11, or gender, F  < 1. An examination of the interactions indicated that all interactions were insignificant: COVID-19 × gender, F(1, 1958) = 1.87, p  = 0.17; and COVID-19 × SES, gender × SES, and COVID-19 × gender × SES, all F’s < 1.

Table 3 . Mean and (SD) of reading comprehension fluency in the among the different SES groups and gender in both years of the study.

The ANCOVA analysis that was run without school as a covariate variable yielded similar trends: a significant main effect of SES, F (2, 1959) = 13.71, p  < 0.001, η 2  = 0.01. All other effects were insignificant: the main effects of COVID-19 m, F (1,1959) = 2.51, p  = 0.11, and gender F < 1, and the interactions of COVID-19 x gender, F(1,1959) = 2.02, p  = 0.16, SES × COVID-19, SES X gender, and SES × COVID-19 × gender (all F’s < 1).

Figure 2 . Comprehension fluency among the different SES levels in both years.

4 Discussion

The acquisition of reading skills stands as a crucial milestone in early elementary education, a complex process that requires significant hours of formal teaching and practice ( Stanovich and West, 1989 ). Against this backdrop, this study aimed to scrutinize the impact of Coronavirus-related school closures on the development of reading fluency and comprehension among second-grade students. Additionally, it aimed to assess the differential impact of COVID-19 on reading skills among second-grade students with varying socioeconomic backgrounds and to explore potential gender differences. This research was spurred by the dearth of comprehensive large-scale studies employing validated reading assessment tools across distinct time periods among children of the same age ( Kesner Baruch et al., 2021 ). The examination of students from the same schools across both pre-pandemic and face-to-face learning periods allowed for a robust evaluation of the gaps in reading acquisition during the COVID-19 era among second-grade learners.

This study explored the influence of COVID-19 on reading and comprehension fluency in second-grade children. The assessment utilized measures of reading fluency for single words (TOWRE; Katzir et al., 2012 , based on Torgeson et al., 1999 ) and comprehension fluency at the sentence level (semantics; Yinon and Shaul, 2017 , based on Hutzler and Wimmer, 2004 ) in two distinct time frames among the second-grade cohort. The measurements occurred both before the onset of Coronavirus-related closures and after their resumption of face-to-face learning. Notably, the two groups of students were drawn from the same schools, exposed to the same educators and curriculum, with the sample adjusted for varying SES levels.

Surprisingly, the results demonstrated no significant disparities in reading fluency between second-grade students assessed before the pandemic in 2019 and those evaluated after the closures in 2020. A plausible explanation for the absence of discrepancies in fluency between these periods pertains to the characteristics of Hebrew orthography. The initial phases of reading acquisition in first grade encompass learning shallow pointed Hebrew, which facilitates the rapid assimilation of the correspondence between letters and sounds due to the provision of comprehensive phonological information ( Share and Levin, 1999 ). As a result, most children become proficient decoders by the end of first grade ( Lipka et al., 2016 ). Crucially, the two cohorts of second-grade students in this study had already acquired these foundational decoding skills during their first-grade year, preceding the pandemic’s advent. This suggests that while remote learning took place during their second-grade year, it did not notably impact the overall fluency and comprehension of these second graders as a whole.

When examining the SES effect, which focused on the differential effects of COVID-19 on reading among second-grade students of varied socioeconomic backgrounds, the study unearthed a significant SES impact on both word-reading fluency and comprehension at the sentence level. The findings highlighted that lower SES corresponded to lower reading and comprehension fluency. Moreover, a noteworthy interaction emerged specifically for reading fluency, rather than comprehension fluency, among students from diverse SES backgrounds. This interaction stemmed from a considerable decline in word-reading fluency and comprehension fluency within children from low SES during the pandemic, in contrast to their higher SES counterparts.

This decline is notable given the widely established SES-based disparities in reading fluency and comprehension ( Burkam et al., 2004 ; Christodoulou et al., 2017 ). The pandemic exacerbated these gaps, revealing that children from low SES backgrounds faced substantial challenges during remote learning, potentially due to limited access to digital resources, reduced parental support, and heightened familial stress. The substantial decrease in reading fluency and comprehension abilities among low-SES children underscores the urgent need for targeted interventions to mitigate the amplified disparities brought about by the pandemic.

To conclude, the study contributes to our understanding of the ramifications of COVID-19-induced school closures on reading acquisition. The investigation suggests that the impact on reading skills might be mediated by prior decoding proficiency and underlines the significance of mitigating socioeconomic disparities. The findings underscore the urgency of tailored educational support to bridge the gaps that have emerged during the pandemic, particularly among students from low-SES backgrounds.

The observed widening gap in reading fluency and comprehension between children of low SES and those of medium-high SES during 2020 underscores a significant concern within the educational landscape ( Burkam et al., 2004 ; Christodoulou et al., 2017 ). This finding highlights a pressing need for understanding the factors contributing to this phenomenon in the context of the COVID-19 pandemic. Several plausible explanations for this widening disparity emerge from the current study’s findings.

One conceivable explanation for the increased gap is rooted in the altered learning environment precipitated by school closures due to the pandemic. The significant reduction in the school day’s duration, coupled with the reliance on digital learning platforms for curriculum delivery, has had varying consequences for different student populations ( Hall et al., 2020 ; Kuhfeld et al., 2020 ). Notably, the majority of second-grade children lack the autonomy required for effective engagement with digital tools, necessitating greater parental involvement. However, parents from low SES backgrounds, who might face financial concerns and time constraints, may have struggled to provide the necessary support for their children’s remote learning ( Giovannella et al., 2020 ; Kruszewska et al., 2020 ; Letzel et al., 2020 ). This lack of adequate support could potentially contribute to the observed widening gap.

Furthermore, households with low SES often face challenges related to digital access and availability ( UNESCO, 2020 ). Reports from teachers in low-SES schools corroborate this, revealing that many students lacked access to computers during remote learning ( Dotan et al., 2021 ). This digital divide could have amplified the gap in reading fluency and comprehension skills, as students without access to digital tools were likely further marginalized during remote learning.

The confluence of these factors, coupled with the abrupt transition to remote learning, might have compounded the challenges faced by students from low SES backgrounds. This combined effect likely contributed to the significant decline in reading fluency and comprehension abilities among these students. This explanation finds reinforcement in a study by Domingue et al. (2021) that revealed the impact of SES on oral reading fluency growth during the COVID-19 period, where low SES students experienced a decline compared to the previous year.

Interestingly, during the pandemic, reading comprehension fluency improved among children of medium-high SES. This could be attributed to the comprehensive support these students received at home, allowing them to capitalize on one-on-one learning opportunities with parents or older siblings. This observation emphasizes the advantages of tailored support in affluent households.

In addition, while no significant gender differences were found in general, an unexpected effect of the pandemic was observed on boys. Previous literature has highlighted gendered experiences in education, with girls often encouraged more to read and boys receiving more opportunities for computing ( Eccles et al., 1993 ). The pandemic-induced shift to remote learning could have impacted boys’ confidence and interest in computing-related learning, thereby affecting their academic performance. Conversely, the superior reading proficiency exhibited by girls on average ( Logan and Johnston, 2010 ) and their affinity for reading could have helped them adapt better to self-regulated, computer-based learning.

The findings underscore the significance of addressing the “Matthew effect” ( Stanovich, 1986 ) in the context of the pandemic-induced disparities. The trajectory of reading skill development may exacerbate differences over time, warranting strategic efforts to narrow these gaps. It is crucial to consider the varied impact of remote learning on different student populations and their unique challenges.

This study has several limitations, although there was a large diverse sample from different SES there were no boys in the high SES group and therefore gender differences were examined only in the medium and low SES groups. In addition, all the children were Hebrew speaking children thus the effect of school closure was not examined among bilingual children or children from different minorities, future studies should examine the long-term effect of the COVID and school closure among different types of population, and at various ages to examine the effect at different stages of reading. Furthermore, only one aspect of comprehension was examined which may limit our understanding of the effect of COVID and school closure, this topic should be further examined as well.

In conclusion, the study highlights the importance of targeted interventions to address the widening gaps exacerbated by the pandemic, particularly among students from low SES backgrounds, as well as gender differences. The repercussions of learning loss and increased stress and anxiety during the pandemic cannot be ignored. Educators and policymakers must channel resources and efforts toward supporting these vulnerable populations to ensure equitable academic outcomes. An exploration of the pandemic’s impact on diverse populations will be integral to comprehending its full educational implications.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Ethics Committee University of Haifa Faculty of Education Chief scientist ministry of Education Israel. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

SS: Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. OL: Writing – review & editing, Methodology, Conceptualization. DT-C: Writing – original draft, Methodology. AB: Writing – review & editing, Data curation. SD: Writing – review & editing, Data curation.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was founded by the chief scientist of the ministry of education, Israel.


We would like to thank the Edmond J. Safra Foundation for their generous support and Tami Katzir for her helpful insights. In addition, great appreciation is conveyed to the students and teachers who participated in the present study.

Conflict of interest

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

Publisher’s note

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

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Keywords: COVID-19 pandemic, reading acquisition, reading fluency, comprehension, socioeconomic status, gender differences

Citation: Shaul S, Lipka O, Tal-Cohen D, Bufman A and Dotan S (2024) The impact of school closures during the COVID-19 pandemic on reading fluency among second grade students: socioeconomic and gender perspectives. Front. Psychol . 15:1289145. doi: 10.3389/fpsyg.2024.1289145

Received: 05 September 2023; Accepted: 20 June 2024; Published: 05 July 2024.

Reviewed by:

Copyright © 2024 Shaul, Lipka, Tal-Cohen, Bufman and Dotan. 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: Shelley Shaul, [email protected]

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.

“They’re coming in and they don’t know how to play.”

“I had some kids who went on to kindergarten who still did not know a triangle.”

“I can’t tell you the number of families who say their kids are anxious or depressed — and they’re little ones, 4 or 5.”

The Youngest Pandemic Children Are Now in School, and Struggling

Teachers this year saw the effects of the pandemic’s stress and isolation on young students: Some can barely speak, sit still or even hold a pencil.

Claire Cain Miller

By Claire Cain Miller and Sarah Mervosh

The pandemic’s babies, toddlers and preschoolers are now school-age, and the impact on them is becoming increasingly clear: Many are showing signs of being academically and developmentally behind.

Interviews with more than two dozen teachers, pediatricians and early childhood experts depicted a generation less likely to have age-appropriate skills — to be able to hold a pencil, communicate their needs, identify shapes and letters, manage their emotions or solve problems with peers.

A variety of scientific evidence has also found that the pandemic seems to have affected some young children’s early development . Boys were more affected than girls, studies have found .

“I definitely think children born then have had developmental challenges compared to prior years,” said Dr. Jaime Peterson, a pediatrician at Oregon Health and Science University, whose research is on kindergarten readiness. “We asked them to wear masks, not see adults, not play with kids. We really severed those interactions, and you don’t get that time back for kids.”

The pandemic’s effect on older children — who were sent home during school closures, and lost significant ground in math and reading — has been well documented. But the impact on the youngest children is in some ways surprising: They were not in formal school when the pandemic began, and at an age when children spend a lot of time at home anyway.

The early years, though, are most critical for brain development. Researchers said several aspects of the pandemic affected young children — parental stress, less exposure to people, lower preschool attendance, more time on screens and less time playing.

Yet because their brains are developing so rapidly, they are also well positioned to catch up, experts said.

The youngest children represent “a pandemic tsunami” headed for the American education system, said Joel Ryan, who works with a network of Head Start and state preschool centers in Washington State, where he has seen an increase in speech delays and behavioral problems.

Not every young child is showing delays. Children at schools that are mostly Black or Hispanic or where most families have lower incomes are the most behind, according to data released Monday by Curriculum Associates , whose tests are given in thousands of U.S. schools. Students from higher-income families are more on pace with historical trends.

But “most, if not all, young students were impacted academically to some degree,” said Kristen Huff, vice president for assessment and research at Curriculum Associates.

Recovery is possible, experts said, though young children have not been a main focus of $122 billion in federal aid distributed to school districts to help students recover.

“We 100 percent have the tools to help kids and families recover,” said Catherine Monk, a clinical psychologist and professor at Columbia, and a chair of a research project on mothers and babies in the pandemic. “But do we know how to distribute, in a fair way, access to the services they need?”

What’s different now?

“I spent a long time just teaching kids to sit still on the carpet for one book. That’s something I didn’t need to do before.”

“We are talking 4- and 5-year-olds who are throwing chairs, biting, hitting, without the self-regulation.”

Brook Allen, in Martin, Tenn., has taught kindergarten for 11 years. This year, for the first time, she said, several students could barely speak, several were not toilet trained, and several did not have the fine motor skills to hold a pencil.

Children don’t engage in imaginative play or seek out other children the way they used to, said Michaela Frederick, a pre-K teacher for students with learning delays in Sharon, Tenn. She’s had to replace small building materials in her classroom with big soft blocks because students’ fine motor skills weren’t developed enough to manipulate them.

Michaela Frederick, a preschool teacher, plays with a stacking toy with a student.

Michaela Frederick, a pre-K teacher in Sharon, Tenn., playing a stacking game with a student.

Aaron Hardin for The New York Times

A child plays with a plastic toy with his fingers.

Preschoolers do not have the same fine motor skills as they did prepandemic, Ms. Frederick said.

Perhaps the biggest difference Lissa O’Rourke has noticed among her preschoolers in St. Augustine, Fla., has been their inability to regulate their emotions: “It was knocking over chairs, it was throwing things, it was hitting their peers, hitting their teachers.”

Data from schools underscores what early childhood professionals have noticed.

Children who just finished second grade, who were as young as 3 or 4 when the pandemic began, remain behind children the same age prepandemic, particularly in math, according to the new Curriculum Associates data. Of particular concern, the students who are the furthest behind are making the least progress catching up.

The youngest students’ performance is “in stark contrast” to older elementary school children, who have caught up much more, the researchers said. The new analysis examined testing data from about four million children, with cohorts before and after the pandemic.

Data from Cincinnati Public Schools is another example: Just 28 percent of kindergarten students began this school year prepared, down from 36 percent before the pandemic, according to research from Cincinnati Children’s Hospital.

How did this happen?

“They don’t have the muscle strength because everything they are doing at home is screen time. They are just swiping.”

“I have more kids in kindergarten who have never been in school.”

One explanation for young children’s struggles, childhood development experts say, is parental stress during the pandemic .

A baby who is exposed to more stress will show more activation on brain imaging scans in “the parts of that baby’s brain that focus on fear and focus on aggression,” said Rahil D. Briggs, a child psychologist with Zero to Three, a nonprofit that focuses on early childhood. That leaves less energy for parts of the brain focused on language, exploration and learning, she said.

During lockdowns, children also spent less time overhearing adult interactions that exposed them to new language, like at the grocery store or the library. And they spent less time playing with other children.

Kelsey Schnur, 32, of Sharpsville, Pa., pulled her daughter, Finley, from child care during the pandemic. Finley, then a toddler, colored, did puzzles and read books at home.

But when she finally enrolled in preschool, she struggled to adjust, her mother said. She was diagnosed with separation anxiety and selective mutism.

“It was very eye-opening to see,” said Ms. Schnur, who works in early childhood education. “They can have all of the education experiences and knowledge, but that socialization is so key.”

Preschool attendance can significantly boost kindergarten preparedness, research has found . But in many states, preschool attendance is still below prepandemic levels. Survey data suggests low-income families have not returned at the same rate as higher-income families.

“I have never had such a small class,” said Analilia Sanchez, who had nine children in her preschool class in El Paso this year. She typically has at least 16. “I think they got used to having them at home — that fear of being around the other kids, the germs.”

Time on screens also spiked during the pandemic — as parents juggled work and children cooped up at home — and screen time stayed up after lockdowns ended. Many teachers and early childhood experts believe this affected children’s attention spans and fine motor skills. Long periods of screen time have been associated with developmental delays .

Heidi Tringali, an occupational therapist in Charlotte, N.C., holds hands with a child who is standing on a ball.

Heidi Tringali, an occupational therapist in Charlotte, N.C., playing with a patient.

Travis Dove for The New York Times

A child in a playroom holds a swing, with a bouncy ball in the foreground.

Children are showing effects of spending time on screens, Ms. Tringali said, including shorter attention spans, less core strength and delayed social skills.

Heidi Tringali, a pediatric occupational therapist in Charlotte, N.C., said she and her colleagues are seeing many more families contact them with children who don’t fit into typical diagnoses.

She is seeing “visual problems, core strength, social skills, attention — all the deficits,” she said. “We really see the difference in them not being out playing.”

Can children catch up?

“I’m actually happy with the majority of their growth.”

“They just crave consistency that they didn’t get.”

It’s too early to know whether young children will experience long-term effects from the pandemic, but researchers say there are reasons to be optimistic.

“It is absolutely possible to catch up, if we catch things early,” said Dr. Dani Dumitriu, a pediatrician and neuroscientist at Columbia and chair of the study on pandemic newborns. “There is nothing deterministic about a brain at six months.”

There may also have been benefits to being young in the pandemic, she and others said, like increased resiliency and more time with family .

Some places have invested in programs to support young children, like a Tennessee district that is doubling the number of teaching assistants in kindergarten classrooms next school year and adding a preschool class for students needing extra support.

Oregon used some federal pandemic aid money to start a program to help prepare children and parents for kindergarten the summer before.

For many students, simply being in school is the first step.

Sarrah Hovis, a preschool teacher in Roseville, Mich., has seen plenty of the pandemic’s impact in her classroom. Some children can’t open a bag of chips, because they lack finger strength. More of her students are missing many days of school, a national problem since the pandemic .

But she has also seen great progress. By the end of this year, some of her students were counting to 100, and even adding and subtracting.

“If the kids come to school,” she said, “they do learn.”

challenges for education during the pandemic an overview of literature

​Why School Absences Have ‘Exploded’ Almost Everywhere

The pandemic changed families’ lives and the culture of education: “Our relationship with school became optional.”

By Sarah Mervosh and Francesca Paris

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Internet of things with deep learning techniques for pandemic detection: a comprehensive review of current trends and open issues.

challenges for education during the pandemic an overview of literature

1. Introduction

1.1. some of the key contributions of this paper are listed as follows.

  • A thorough review backs up the role of IoT applications in prevalence pandemic controls and prevention, which motivates and emphasizes the need for a reliable detection strategy to combat the pandemic
  • A thorough review backs up the role of DL techniques usable for pandemic diagnosis and detection
  • Current trend of IoT-DL contribution in pandemic detection and control
  • The provision of extensive discussion and open issues for developing novel IoT-DL approaches pandemic detection.

1.2. Study Organization

2. internet of things (iot)-based pandemic detection systems, 2.1. review of existing iot-based systems for pandemic control, 2.2. applications and descriptions of iot technologies, including wearables, and sensors in covid-19, 3. deep learning techniques for pandemic detection, 3.1. overview of dl techniques suitable for pandemic detection, 3.2. integration of dl with iot devices for real-time monitoring in a time-series data, 4. current trend in pandemic detection based on iot-dl approach, 4.1. data gathering, 4.2. exploration of recent advancements in iot and dl for pandemic detection, 5. state-of-the-art review of literature, 5.1. discussion of findings on the implementation of optimal iot-dl techniques for pandemic detection, 5.2. research part and open issues, 6. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest, technical terms and abbreviations.

1AIArtificial Intelligence
2CNNConvolutional Neural Network
3COVID-192019 Novel Coronavirus
4DLData Learning
5IoMTInternet of Medical Things
6IoTInternet of Things
7MLMachine Learning
8MMMathematical Models
9MOAMetaheuristic Optimisation Algorithm
10SCSmart Contact
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Click here to enlarge figure

Refs.ApplicationsDescription of the Roles Played in the COVID-19 Pandemic Control
[ , , ]Internet-connected healthcare centers and data collectionWearable health monitoring devices in real-time surveillance. To support pandemics like COVID-19, hospital facilities require a fully integrated network for the adoption of IoT.
[ , ]Communicate medical staff during any emergencyPatients and staff would be able to respond more swiftly and efficiently when necessary thanks to this integrated network.
[ , ]Transparent COVID-19 treatmentThe patients can avail the benefits offered without any partiality and favors
[ ]Automated treatment processThe selection of effective treatment modalities facilitates the proper management of cases. Provides healthcare support whenever and wherever needed. Telemedicine as a tool to stop infections and manage viral transmission
[ , ]Telehealth consultationSpecifically, to use well-connected teleservices to make therapy available to those in need in rural areas.
[ , ]Wireless healthcare network to identify COVID-19 patientSmartphones can be equipped with a variety of genuine applications, and sophisticated tracking of affected individuals can facilitate a more efficient and successful identification process.
[ ]Smart tracing of infected patientsThe impactful tracing of patients ultimately strengthened the service providers to handle the cases more smartly
[ ]Real-time information during the spread of this infectionAccurate case handling and timely information sharing are made possible by the well-informed and connected locations, channels, and other aspects of IoT-based devices created a wearable, IoT-based quarantine band that may be used to track and identify fugitives in real time.
[ ]Rapid COVID-19 screeningThe correct diagnosis would be tried using smart, connected therapy equipment as soon as the case is received. In the end, this enhances the overall quality of the screening procedure.
[ ]Identify innovative solutionThe ultimate objective is the overall standard of supervision. It can be accomplished by establishing innovations as ground-level successes.
[ ]Connect all medical tools and devices through the internetIoT connects all medical equipment and gadgets via the internet to provide real-time information during COVID-19 treatment.
[ ]Accurate forecasting of virus with the help of data analytics toolsForecasting and predicting the rate of infection and making disease diagnoses. Utilizing statistical methods can aid in forecasting the future state of affairs based on the data report that is currently accessible. It will also assist in planning for a better working environment for the government, physicians, academics, and other professionals.
Search SourcesSearch Queries
Scopus“Internet of things” OR “IoT” AND “pandemic” OR “COVID-19” AND “Deep learning” OR “DL” AND “Optimisation” OR “Optimization” OR “Optimised” OR “Optimized” AND PUBYEAR > 2019 AND PUBYEAR < 2025
WoS“Internet of things” OR “IoT” AND “pandemic” OR “COVID-19” AND “Deep learning” OR “DL” AND “Optimisation” OR “Optimization” OR “Optimised” OR “Optimized” AND PUBYEAR > 2019 AND PUBYEAR < 2025
INCLUSIONConference PaperA well-defined IoT-DL-based paper concentrating on pandemic especially, covering processes, data gathering methods, results and analysis approaches, as well as the conclusions that form the basis for an oral presentation.
Research-based Chapter in BookStudy having a well-defined IoT-DL-based paper concentrating on pandemic specifically, covering procedures, data gathering strategies, analysis techniques, and findings.
Research PaperThe paper aimed to investigate specific research problems related to IoT-DL-based paper applications on pandemic
EXCLUSIONDuplicated papersThe identical document that occurs more than once
Non-research papersThis article is not scientific in nature. Editorial notes, remarks, comments review and related papers were eliminated
Non-related papersThe problem being studied outside the coverage of this work.
Non English papersThe paper was not written in English
Implicitly related papersThe paper does not directly express the research focus pandemic and the use of DL approach.
Ref.Study GoalsApproach UsedContribution Made
[ ]To present a novel model for enhancing the standard of treatment in smart healthcare systems (SHS) by integrating AI and IoT technologies.Introducing an upgraded particle swarm optimization-long short-term memory (PSO-LSTM) algorithm to optimize the IoT-based SHS model. Comparing the performance of PSO with PSO-LSTM for classifying patient medical data. Tuning several metrics and benchmarks to achieve the highest performance in processing patient data. Evaluating the proposed model using test sets to predict patient health risks.The study demonstrates that the PSO-LSTM algorithm provides a more satisfactory performance with higher efficiency in classifying patient medical data, achieving an accuracy of 92.5%. This indicates a more secure, reliable, and improved patient satisfaction experience. The integration of AI and IoT in smart healthcare systems offers advanced methods for managing medical records and optimizing patient data processing performance, thereby enhancing healthcare services.
[ ]To introduced a pioneering hybrid DL model for precise energy consumption prediction, aiming to optimize energy efficiency in residential and commercial buildings.Utilizing IoT-enabled smart meter data to achieve granular energy consumption forecasts. Developing a hybrid DL model that combines CNNs and LSTM units. Conducting a comparative analysis against established DL models to evaluate performance. Focusing on accurately predicting weekly average energy usage in both residential and commercial spaces.The study showcases a novel model architecture that demonstrates superior performance in energy consumption forecasting, particularly excelling in predicting weekly average energy usage. The hybrid model’s demonstrated capability underscores its potential to drive sustainable energy utilization and provide invaluable guidance for more energy-efficient futures. This innovative approach offers significant promise in guiding tailored energy management strategies, thereby fostering optimized energy consumption practices in buildings.
[ ]The main aim of the research was to develop an efficient real-time IoT-based COVID-19 monitoring and prediction system using a DL model. The goal is to monitor COVID-19 patients, report health issues immediately, and predict COVID-19 suspects in the early stages.Utilizing IoT-based healthcare systems for real-time monitoring and prediction.
Collecting symptomatic patient data from sensors. Selecting effective parameters using the Modified Chicken Swarm Optimization (MCSO) approach.
Employing a hybrid Deep Learning model called Convolution and graph LSTM (ConvGLSTM) for COVID-19 prediction. Implementing four stages: data collection, data analysis (feature selection), diagnostic system (DL model), and cloud system (storage).
The developed model is experimented with using a dataset from Srinagar, evaluating parameters such as accuracy, precision, recall, F1 score, RMSE, and AUC. The study demonstrates that the proposed model is effective and superior to traditional approaches in early identification of COVID-19.
[ ]The research introduced a novel AI-based mechanism for optimizing threat mitigation in IoT banking systems, addressing the growing vulnerabilities in this critical sector.Developing an AI-based mechanism leveraging a Deep Neural Architecture known as Pointer Networks. Focusing on threat identification and mitigation in IoT banking systems, ensuring high precision and recall. Conducting extensive threat-specific evaluations to test the mechanism’s performance across various scenarios. Implementing scalability testing to validate the mechanism’s practical applicability across varying sizes of IoT ecosystems.Demonstrating a robust defense mechanism with a precision of 0.88, a balanced recall of 0.79, and an F1 score of 0.83. Proving the mechanism’s versatility and high performance in detecting and mitigating different types of cyber threats: Malware: Precision: 0.89, Recall: 0.82, F1 score: 0.85. Denial of Service (DoS) attacks: Precision: 0.87, Recall: 0.78, F1 score: 0.82. Unauthorized access attempts: Precision: 0.90, Recall: 0.81, F1 score: 0.85. Ensuring the mechanism maintains high precision and F1 score values across different sizes of IoT ecosystems, validating its scalability and practical applicability.
[ ]To develop a sophisticated and effective epidemiological surveillance system for COVID-19 that overcomes the limitations of conventional approaches by leveraging IoT and advanced data analytics.The developed framework created the SEIR-Driven Semantic Integration Framework (SDSIF) designed to handle diverse data sources using IoT technology.
COVID-19 Ontology: Develop an extensive COVID-19 ontology to enable unmatched data interoperability and semantic inference.
Data Integration and Analytics: Facilitate real-time data integration and utilize RNN for advanced analytics, anomaly detection, and predictive modeling. Scalability and Flexibility: Ensure the framework is scalable and flexible to adapt to various healthcare environments and geographical regions.
Evaluation: Assess the performance of SDSIF using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared score.
The SDSIF framework revolutionizes COVID-19 epidemiological surveillance by integrating and analyzing data in real-time, offering unparalleled data interoperability and semantic inference through its innovative ontology. This framework enhances predictive modeling and anomaly detection capabilities, proving highly accurate and precise in predicting COVID-19 trends. The rigorous evaluation metrics demonstrate the framework’s effectiveness, with an RMSE of 8.70, MSE of 3.03, and an exceptional R-squared score of 0.99, highlighting its robustness in explaining disease data variations. This contribution marks a significant advancement in managing and responding to the COVID-19 pandemic and potentially other epidemiological crises.
[ ]The goal of the study was to develop a remote diagnostic system, called IFCnCov, for diagnosing COVID-19 patients in real-time. The system integrates IoT, fog computing (FC), cloud computing (CC), ensemble learning (EL), and DL principles to achieve accurate diagnosis remotely.IFCnCov was designed as a two-layered architecture, incorporating DL approaches trained on two different datasets: a symptom-based dataset and a chest X-ray imaging dataset sourced from the Kaggle repository. IoT, FC, and CC Integration: The system leverages the integration of IoT, FC, and CC principles to address latency, bandwidth, energy consumption, security, and privacy issues associated with remote diagnosis.
Ensemble Learning and DL: EL and DL techniques are utilized for accurate diagnosis, with DL models trained on the symptom-based and chest x-ray imaging datasets. The performance of IFCnCov was evaluated using various evaluative measures, including accuracy, precision, sensitivity, specificity, and F1-scores. Validation was conducted on network parameters such as scalability, energy consumption, network utilization, jitter, processing time, throughput, and arbitration time.
Enhanced Accuracy: IFCnCov achieves significantly high accuracies, precision, sensitivity, specificity, and F1-scores in both stages of diagnosis, outperforming some other state-of-the-art works. Validation of Network Parameters: The study validates the performance of IFCnCov in terms of various network parameters, demonstrating its scalability, energy efficiency, processing speed, and overall effectiveness.
[ ]The goal of this study was to comprehensively explore the intersection of cloud computing and AI in education and assess their combined impact on accessibility, efficiency, and quality of learning. The study aimed to investigate how these technologies enhance personalized learning experiences, increase user capacity, reduce administrative errors, improve scalability, and enrich overall learning experiences.The study employs a mixed-research design to investigate the convergence of cloud computing and AI in education. It identifies improvements in educational content personalization attributed to AI and enhancements in simultaneous user capacity facilitated by cloud computing. The methodology involves analyzing data to quantify the extent of improvement in accessibility, efficiency, and quality of learning resulting from the integration of these technologies.The study contributes to the understanding of the synergistic effects of cloud computing and AI in education by providing empirical evidence of their impact on various aspects of teaching and learning. It reports a 25% improvement in educational content personalization and a 60% increase in simultaneous user capacity, along with reductions in administrative errors and improvements in scalability. By comparing these findings with previous research, the study positions itself as a critical resource for guiding future developments and improvements in the education sector in the context of a digitally advanced world.
[ ]The primary goal of this study was to design and develop an integrated system of audio and video sensors, leveraging the IoT, to recognize and monitor coughing and sneezing, which are key symptoms of COVID-19. The system aims to provide real-time detection and alerting capabilities to support early intervention and containment measures, ultimately reducing the spread of the virus.One way to get around these setbacks was through sensor integration. Furthermore, it raises the accuracy of event recognition. We suggested a real-time integrated IoT architecture to enhance the outcomes of coughing and sneezing detection because low-cost audio and video sensors are widely available. A cloud computing infrastructure was integrated with edge computing. Edge computing involves the camera and microphone being embedded with a DL engine and being connected to the internet.A real-time coughing and sneezing activities are detected by edge computing by feeding it audio and video data. Comparing the accuracy and recall of the cloud detector to audio-only and video-only detectors, the cloud computing technology, which is built on the Amazon Web Service (AWS), improves both on average by 43% and 15%, respectively. The F-score increased 1.24 times on average.
[ ]The paper focused on designing and development of “Smart COVIDNet”, an IoT-based framework for predicting COVID-19 disease. The framework leverages an ensemble of deep learning models with attentive and adaptive mechanisms to improve the accuracy and efficiency of COVID-19 diagnosis.The study used IoT devices to gather real-time personal health data from participants, including temperature, heart rate, and breathing rate. Preprocessing procedures are used to collected data to guarantee quality and eliminate noise, preparing it for analysis. The system makes use of a group of DL models, each with a focus on a distinct area of the data. Transformer-based architectures, RNNs, and CNNs are some examples of these models. In order to improve the model’s capacity to recognize significant patterns connected to COVID-19, an attention mechanism was employed to concentrate on pertinent aspects in the data. By adding fresh data to the models on a regular basis, the framework adjusts to shifting patterns in the data and gradually increases the forecast accuracy of the models. An ensemble approach was used to integrate the outputs from individual models to create a final forecast that is anticipated to be more accurate than the individual predictions.High precision and efficacy in COVID-19 infection prediction are demonstrated by the Smart COVIDNet platform. Early and accurate COVID-19 diagnosis can be achieved with a robust system that combines attentive and adaptive DL techniques with real-time data collection from IoT devices. By facilitating accurate and timely disease prediction, the study finds that Smart COVIDNet can be an effective tool for managing and containing the spread of COVID-19.
[ ]The study’s objective was to increase COVID-19 detection efficiency and accuracy by utilizing IoT data and sophisticated deep learning techniques with a recurrent neural network (RERNN) improved by recalling and optimized with the Golden Eagle Optimization (GEO) algorithm.The system collected real-time health data from people using IoT sensors, such as temperature, oxygen saturation, heart rate, and respiration rate. The gathered data was preprocessed to guarantee quality and eliminate noise, preparing it for DL analysis. Over time, the RERNN, a specialized RNN variation, was developed to improve memory recall capacities and increase its efficacy in finding patterns connected to COVID-19. The performance of the RERNN and its hyperparameters were optimized using the GEO Algorithm. Inspired by the hunting tactics of golden eagles, this optimization technique aims to increase the neural network’s accuracy and convergence speed. Using preprocessed IoT data, the RERNN model was trained, and standard metrics including accuracy, precision, recall, and F1-score were used to assess the model’s performance.High accuracy and efficiency in COVID-19 detection are demonstrated by the IoT-based COVID-19 detection system that uses the RERNN optimized with the GEO algorithm. The study comes to the conclusion that combining IoT data with cutting-edge deep learning and optimization methods can greatly improve COVID-19 early identification and treatment. The suggested system has demonstrated potential in giving medical practitioners a dependable and prompt diagnosis tool, improving control and reducing the spread of the illness.
[ ]Authors developed a smart IoT-based monitoring system for COVID-19 utilizing hybrid DL models aimed to enhance the monitoring, detection, and management of COVID-19 by combining various DL techniques and leveraging IoT technology.The system used IoT devices to continuously monitor and gather people’s real-time health data, including body temperature, heart rate, and breathing rate. To assess the data gathered, the study uses a hybrid model that combines several deep learning approaches. In order to improve the focus on pertinent data aspects and increase the model’s accuracy in finding COVID-19-related patterns, this contains CNNs, RNNs, and Attention Mechanisms. Preprocessing is done on the gathered data to guarantee quality and eliminate noise. This data was processed by the hybrid model, which uses it to derive insightful conclusions and precise forecasts. Real-world scenarios are used to implement and evaluate the performance of the Internet of Things-based monitoring system. Standard performance indicators including accuracy, precision, recall, and F1-score are used to assess the model’s efficacy.This study’s hybrid DL model-based smart IoT monitoring system shows excellent efficacy in COVID-19 detection and monitoring. IoT technologies and cutting-edge DL methods work together to offer a reliable solution for early COVID-19 symptom detection and real-time health monitoring. According to the study’s findings, a system like this can greatly help with the effective treatment and control of COVID-19 by giving medical practitioners immediate and precise health insights to help them make decisions.
[ ]The study aimed to utilize DL models’ capacity to evaluate health data gathered from IoT devices to identify COVID-19 infections precisely and promptly. Authors created an effective COVID-19 identification system by integrating DL methods with IoT technology.The system uses IoT devices to collect people’s real-time health data. This includes variables like oxygen saturation levels, heart rate, breathing rate, and body temperature. Preprocessing procedures are used to clean and standardize the obtained data, making it ready for DL research. The preprocessed data is processed and analyzed using several DL models. These algorithms have been trained to identify characteristics and patterns that point to COVID-19 infection. A dataset of health-related factors is used to train the DL models, and optimization techniques are used to increase the models’ precision and effectiveness. The models are adjusted to enhance their capacity to recognize COVID-19 cases. Standard metrics like accuracy, precision, recall, and F1-score are used to assess the effectiveness of the DL-based identification system. To evaluate the system’s dependability and efficacy, it was put through a number of situations.The study comes to the conclusion that the DL-based IoT system created for COVID-19 identification is incredibly accurate and efficient. Real-time monitoring and early COVID-19 infection identification are made possible by the integration of DL models with IoT technology, giving medical professionals a useful tool. Based on quick and accurate diagnosis capabilities, the results show that such a system may greatly enhance COVID-19 management and control.
[ ]The study was to provide a DL system for early COVID-19 evaluation that was based on the IoT. The system seeks to identify and evaluate COVID-19 infections early on by utilizing the capabilities of IoT devices and cutting-edge DL algorithms.The framework uses a variety of IoT devices, including smart devices and wearable sensors, to continuously gather personal health data from users. Sophisticated DL models were utilized for the purpose of data analysis. The purpose of these models was to find trends and abnormalities that point to COVID-19 infection. Prior to feeding the input into the DL models, the quality and relevancy of the data gathered from IoT devices were checked through preprocessing. A dataset comprising COVID-19 positive and negative examples is used to train the DL models. To evaluate the models’ dependability and accuracy in identifying COVID-19, validation was carried out.The study concluded that the DL framework based on the IoT was useful for early COVID-19 detection. IoT device integration makes it possible to monitor health in real time, and DL models use the data gathered to accurately detect COVID-19. Early intervention and control measures are essential for controlling the virus’s transmission and enhancing patient outcomes, and the framework appears to be promising in this regard. This could enhance prompt medical intervention and slow the virus’s spread.
[ ]The study provided an IoT-integrated ensemble DL framework for COVID-19 automated diagnosis. The objective was to improve COVID-19 detection efficiency and accuracy by utilizing IoT devices for real-time data gathering and analysis and merging many DL models.Real-time health data, such as body temperature, respiration patterns, and other vital signs important for COVID-19 diagnosis, are collected by the framework using IoT sensors. Many DL models are utilized to examine the gathered information. These models comprise, among others, CNNs and RNNs. To increase the overall diagnostic accuracy, the outputs of various DL models are combined using an ensemble technique. To combine the predictions from several models, methods like weighted averaging and voting procedures are used. Preprocessing is done on the gathered IoT data to reduce noise and standardize the inputs for improved model performance. Labeled COVID-19 positive and negative samples are included in a varied dataset that is used to train the DL models. The ensemble model’s performance was verified through the use of metrics like accuracy, precision F1 score and recall.The study came to the conclusion that the accuracy and dependability of automated COVID-19 diagnosis are greatly increased when the ensemble DL framework and IoT technology are used together. Using the advantages of several models, the ensemble technique increases the detection system’s robustness. Because of the integration with IoT devices, continuous and real-time monitoring is made possible, which makes the system useful for accurate and timely COVID-19 detection. With prompt identification and action, the suggested paradigm shows promise for helping healthcare systems manage the pandemic more skillfully.
[ ]The study created an intelligent COVID-19 monitoring system by integrating wearable IoT sensors with DL algorithms. The goal was to improve patient care and efficiently control the virus’s spread by offering ongoing, real-time surveillance and early detection of COVID-19 symptoms.The framework uses intelligent wearable Internet of Things sensors to continuously gather physiological data from people, including heart rate, body temperature, oxygen saturation, and respiration rate. Superb DL algorithms are applied to the data collection process. The models are trained to identify abnormalities and patterns that point to COVID-19 infection. Preprocessing is done on the data collected by the wearable sensors to guarantee accuracy and consistency. This entails normalizing the data and eliminating noise. A large dataset with both COVID-19 positive and negative events is used to train the DL models. The models’ efficacy in identifying COVID-19 is then evaluated by validating them using a range of performance indicators, including accuracy, precision, recall, and F1 score. Using the continuous data stream analysis from the wearable sensors, the framework allows for real-time monitoring and alarms.The study concluded that the intelligent monitoring framework integrating DL with smart wearable IoT sensors is effective for the early detection and monitoring of COVID-19. The continuous real-time data collection and analysis enhance the ability to detect COVID-19 symptoms promptly, enabling timely medical intervention. The proposed framework demonstrates significant potential in improving patient outcomes and aiding in the control of the COVID-19 pandemic through effective monitoring and early diagnosis.
[ ]The goal of the project was to create a framework for leveraging data from IoT-based wearable devices to remotely monitor COVID-19 patients’ health. The framework analyzes health data using CNNs and metaheuristics in order to provide accurate and ongoing patient health status monitoring.The framework gathers real-time health data from COVID-19 patients using wearable technology. Vital indicators including heart rate, temperature, and oxygen saturation levels are included in this data. The feature selection and data pretreatment procedures are optimized through the use of metaheuristic algorithms. The effectiveness and precision of the data analysis are improved by these algorithms. The preprocessed data is processed and analyzed using CNNs. The purpose of these deep learning models is to find patterns and abnormalities in the medical data that might point to alterations in the patient’s condition. Preprocessing procedures, such as noise reduction and normalization, were applied to the gathered data. Prior to feeding the data into the CNNs, the metaheuristics optimize the selection of pertinent characteristics. Through ongoing analysis of the patient’s medical records and the provision of real-time alarms and status updates, the framework makes remote monitoring possible.The study finds that the suggested framework—which combines CNNs and metaheuristics with IoT-based wearable devices—was useful for remotely keeping an eye on COVID-19 patients’ health. The accuracy and dependability of the health monitoring system are improved by the application of DL techniques and sophisticated optimization algorithms. For COVID-19 patients, the framework offers prompt alerts and insights into their health status, which can result in improved management and intervention techniques.
[ ]The goal of the study was to address the security concerns in electronic healthcare systems, particularly focusing on the protection of sensitive health images transmitted over networks. The study aims to propose and implement a secure lightweight key frame extraction approach and an encryption scheme for ensuring the integrity and confidentiality of COVID-19 CT-images, while also utilizing AI techniques for COVID-19 testing.The methodology involves the identification of security concerns recognizing the need for secure transmission of health images in electronic healthcare systems and understanding the limitations of traditional encryption methods for image data. Development of Secure Lightweight Key Frame Extraction Approach: Proposing and implementing a lightweight key frame extraction approach to ensure the accuracy and privacy protection of e-health services.
Encryption Scheme Development: Building an encryption scheme incorporating a hashing version of the Blum Blum Shub (BBS) generator, known as Hash-BBS (HBBS), to achieve high-grade integrity and confidentiality in the transmission of COVID-19 CT-images. Utilization of AI Techniques for COVID-19 Testing: Applying CNN as an AI technique for COVID-19 testing to enhance secure prediction. Evaluating the proposed framework’s performance compared to alternative security and transfer learning methodologies in terms of security and prediction benchmarks.
Enhanced Security: Introducing a secure lightweight key frame extraction approach and an encryption scheme (HBBS) to ensure the integrity and confidentiality of COVID-19 CT-images transmitted in electronic healthcare systems. Utilization of AI for COVID-19 Testing: Employing CNN for COVID-19 testing to improve secure prediction of COVID-19 cases. Demonstrating through evaluation that the proposed framework outperforms alternative security and transfer learning methodologies, providing reliable transmission of CT-images for COVID-19 patients while meeting strict security and prediction benchmarks.
[ ]The goal of this methodology is to enhance security and safety measures in public gathering places by utilizing IoT technology and DL concepts. Specifically, the aim is to verify the presence of safety items such as masks and gloves, as well as detect body temperature, of individuals entering public spaces.A camera, temperature sensor, and other safety sensors are installed at the entry points of public gathering places. Image processing techniques, coupled with DL algorithms, are employed to analyze the images captured by the camera. This analysis verifies the presence of safety items like masks and gloves. The temperature sensor measures the body temperature of individuals entering the premises. Additionally, the setup includes a sanitizer sprayer that activates when hands are placed in front of it. All sensors are connected to a single-board computer (SBC), such as Raspberry Pi, which processes the sensor data and triggers actions accordingly. If safety requirements are met, the locks are opened. Otherwise, individuals are flagged for further monitoring and disciplinary actions.Enhancing security and safety measures in public gathering places using IoT and DL technologies.
Achieving over 95% object detection accuracy through DL and image processing techniques.
Ensuring worker safety at public places by implementing low-cost safety precautions. Reducing the workload of supervisors and minimizing manpower required for safety monitoring.
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Ajagbe, S.A.; Mudali, P.; Adigun, M.O. Internet of Things with Deep Learning Techniques for Pandemic Detection: A Comprehensive Review of Current Trends and Open Issues. Electronics 2024 , 13 , 2630.

Ajagbe SA, Mudali P, Adigun MO. Internet of Things with Deep Learning Techniques for Pandemic Detection: A Comprehensive Review of Current Trends and Open Issues. Electronics . 2024; 13(13):2630.

Ajagbe, Sunday Adeola, Pragasen Mudali, and Matthew Olusegun Adigun. 2024. "Internet of Things with Deep Learning Techniques for Pandemic Detection: A Comprehensive Review of Current Trends and Open Issues" Electronics 13, no. 13: 2630.

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