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View ratings trends. Add media. Top Positive Review. Sort by: Relevant Newest to Oldest Helpful Rating. Rating 5 stars 6. Timeframe All time Past week Past month Past 6 months Past year. Other Verified purchase. Verified site experience. Contains image or video. English only. Maryann G. Follow Maryann G. Send Message Share Review. It was an absolute pleasure to deal with this service! Highly recommend! Their writers are very patient and got very high writing skills! Comment Thank you Respond as company Helpful Ronald T. Follow Ronald T. The work is awesome! Good work. Really like it. Willard L. Follow Willard L. 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 pandemic? 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? 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.
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.
Our systematic review identified 42 studies on learning progress during the COVID pandemic that met our inclusion criteria. As shown in Fig. 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 , and on 8 August 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 learning deficits, separately for maths and reading and for different school grades. Flow diagram of the study identification and selection process, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses PRISMA guidelines. Table 1 presents all included studies and estimates of COVID 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.
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 learning deficits. 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.
and 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 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. 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. 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. 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. 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. 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. 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. 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 learning deficits and on the variation in learning deficits over time, across different groups of students, and across country contexts.
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. Estimates are pooled using inverse variance weights. Under normal circumstances, students generally improve their performance by around 0. On average, the learning progress of school-aged children has slowed substantially during the pandemic. 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. 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. However, it is important to note that estimates of learning deficits are based on distinct samples of students. Future research should continue to follow the learning progress of cohorts of students in different countries to reveal how learning deficits of these cohorts have developed and continue to develop since the onset of the pandemic. The horizontal axis displays the date on which learning progress was measured. The vertical axis displays estimated learning deficits, expressed in standard deviation s. 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 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 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 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 , 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. Within these categories, estimates are ordered horizontally by school grade.
The shading indicates when in the pandemic a given measure was taken. 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. This difference is statistically significant and robust to dropping estimates from individual countries Supplementary Fig. Each plot shows the distribution of COVID 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. a , Learning subject reading versus maths. b , Level of education primary versus secondary. c , Country income level high versus middle. 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. 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. 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 , 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 , 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 As discussed above, most evidence on COVID 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 learning deficits in these four middle-income countries together on the right and compares them with estimates from high-income countries on the left.
In fact, the three largest estimates of learning deficits in our sample are from middle-income countries Fig. Two years since the COVID 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 pandemic.
This confirms initial concerns that substantial learning deficits would arise during the pandemic 10 , 39 , But our results also suggest that fears of an accumulation of learning deficits as the pandemic continues have not materialized 41 , On average, learning deficits emerged early in the pandemic and have neither closed nor widened substantially. 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 , 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 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 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 , 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 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. 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. 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 Sending motivational text messages successfully limited learning losses in maths and Portuguese in Brazil 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.
We consider all types of primary research, including peer-reviewed publications, preprints, working papers and reports, for inclusion. Moreover, studies have to be in English, Danish, Dutch, French, German, Norwegian, Spanish or Swedish. We identified relevant studies using the following steps. First, we developed a Boolean search string defining the population school-aged children , exposure the COVID pandemic and outcomes of interest learning progress. The full search string can be found in Section 1. 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 Our initial search was conducted on 27 April , and we conducted two forward and backward citation searches of all eligible studies identified in the above steps, on 14 February , and on 8 August , to ensure that our analysis includes recent relevant research. 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. We contacted study authors if any of the above information was missing in the study.
Data extraction was performed by B. and validated independently by A. 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 We distinguish between primary and secondary education. We first consulted the original studies for this information. 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. 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. 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. Third, given that the limited amount of available evidence precludes multivariate or causal analyses, we examine the bivariate association between COVID learning deficits and the months in which learning was measured using a scatter plot Fig.
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. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. The data used in the analyses for this manuscript were compiled by the authors based on the studies identified in the systematic review.
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