research paper about lack of 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: orcid.org/0000-0002-4544-4073 1 , 2 , 3 ,
• Anders M. Bach-Mortensen   ORCID: orcid.org/0000-0001-7804-7958 2 &
• Per Engzell   ORCID: orcid.org/0000-0002-2404-6308 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 ( https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021249944 ), 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).

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.

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.

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.

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.

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 ).

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 .

Pre-registration

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 ( https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021249944 ).

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 ( https://doi.org/10.17605/osf.io/u8gaz ). For our systematic review, we searched the following databases: Coronavirus Research Database ( https://proquest.libguides.com/covid19 ), Education Resources Information Centre database ( https://eric.ed.gov ), International Bibliography of the Social Sciences ( https://about.proquest.com/en/products-services/ibss-set-c/ ), Politics Collection ( https://about.proquest.com/en/products-services/ProQuest-Politics-Collection/ ), Social Science Database ( https://about.proquest.com/en/products-services/pq_social_science/ ), Sociology Collection ( https://about.proquest.com/en/products-services/ProQuest-Sociology-Collection/ ), Cumulative Index to Nursing and Allied Health Literature ( https://www.ebsco.com/products/research-databases/cinahl-database ) and Web of Science ( https://clarivate.com/webofsciencegroup/solutions/web-of-science/ ). We also searched the following preprint and working paper repositories: Social Science Research Network ( https://papers.ssrn.com/sol3/DisplayJournalBrowse.cfm ), Munich Personal RePEc Archive ( https://mpra.ub.uni-muenchen.de ), IZA ( https://www.iza.org/content/publications ), National Bureau of Economic Research ( https://www.nber.org/papers?page=1&perPage=50&sortBy=public_date ), OSF Preprints ( https://osf.io/preprints/ ), PsyArXiv ( https://psyarxiv.com ), SocArXiv ( https://osf.io/preprints/socarxiv ) and EdArXiv ( https://edarxiv.org ).

Code availability

All code needed to replicate our findings is available on the Open Science Framework repository ( https://doi.org/10.17605/osf.io/u8gaz ).

The Impact of COVID-19 on Children. UN Policy Briefs (United Nations, 2020).

Donnelly, R. & Patrinos, H. A. Learning loss during Covid-19: An early systematic review. Prospects (Paris) 51 , 601–609 (2022).

Hammerstein, S., König, C., Dreisörner, T. & Frey, A. Effects of COVID-19-related school closures on student achievement: a systematic review. Front. Psychol. https://doi.org/10.3389/fpsyg.2021.746289 (2021).

Panagouli, E. et al. School performance among children and adolescents during COVID-19 pandemic: a systematic review. Children 8 , 1134 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Patrinos, H. A., Vegas, E. & Carter-Rau, R. An Analysis of COVID-19 Student Learning Loss (World Bank, 2022).

Zierer, K. Effects of pandemic-related school closures on pupils’ performance and learning in selected countries: a rapid review. Educ. Sci. 11 , 252 (2021).

Article   Google Scholar  

König, C. & Frey, A. The impact of COVID-19-related school closures on student achievement: a meta-analysis. Educ. Meas. Issues Pract. 41 , 16–22 (2022).

Storey, N. & Zhang, Q. A meta-analysis of COVID learning loss. Preprint at EdArXiv (2021).

Sterne, J. A. et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ https://doi.org/10.1136/bmj.i4919 (2016).

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

Bloom, H. S., Hill, C. J., Black, A. R. & Lipsey, M. W. Performance trajectories and performance gaps as achievement effect-size benchmarks for educational interventions. J. Res. Educ. Effectiveness 1 , 289–328 (2008).

Hill, C. J., Bloom, H. S., Black, A. R. & Lipsey, M. W. Empirical benchmarks for interpreting effect sizes in research. Child Dev. Perspect. 2 , 172–177 (2008).

Belot, M. & Webbink, D. Do teacher strikes harm educational attainment of students? Labour 24 , 391–406 (2010).

Jaume, D. & Willén, A. The long-run effects of teacher strikes: evidence from Argentina. J. Labor Econ. 37 , 1097–1139 (2019).

Cygan-Rehm, K. Are there no wage returns to compulsory schooling in Germany? A reassessment. J. Appl. Econ. 37 , 218–223 (2022).

Ichino, A. & Winter-Ebmer, R. The long-run educational cost of World War II. J. Labor Econ. 22 , 57–87 (2004).

Cooper, H., Nye, B., Charlton, K., Lindsay, J. & Greathouse, S. The effects of summer vacation on achievement test scores: a narrative and meta-analytic review. Rev. Educ. Res. 66 , 227–268 (1996).

Allington, R. L. et al. Addressing summer reading setback among economically disadvantaged elementary students. Read. Psychol. 31 , 411–427 (2010).

Smith, W. C. Consequences of school closure on access to education: lessons from the 2013–2016 Ebola pandemic. Int. Rev. Educ. 67 , 53–78 (2021).

Andrabi, T., Daniels, B. & Das, J. Human capital accumulation and disasters: evidence from the Pakistan earthquake of 2005. J. Hum. Resour . https://doi.org/10.35489/BSG-RISE-WP_2020/039 (2021).

Parolin, Z. & Lee, E. K. Large socio-economic, geographic and demographic disparities exist in exposure to school closures. Nat. Hum. Behav. 5 , 522–528 (2021).

Goudeau, S., Sanrey, C., Stanczak, A., Manstead, A. & Darnon, C. Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap. Nat. Hum. Behav. 5 , 1273–1281 (2021).

Article   PubMed   Google Scholar  

Bailey, D. H., Duncan, G. J., Murnane, R. J. & Au Yeung, N. Achievement gaps in the wake of COVID-19. Educ. Researcher 50 , 266–275 (2021).

van de Werfhorst, H. G. Inequality in learning is a major concern after school closures. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2105243118 (2021).

Alexander, K. L., Entwisle, D. R. & Olson, L. S. Schools, achievement, and inequality: a seasonal perspective. Educ. Eval. Policy Anal. 23 , 171–191 (2001).

Aucejo, E. M. & Romano, T. F. Assessing the effect of school days and absences on test score performance. Econ. Educ. Rev. 55 , 70–87 (2016).

Gottfried, M. A. The detrimental effects of missing school: evidence from urban siblings. Am. J. Educ. 117 , 147–182 (2011).

Goodman, J. Flaking Out: Student Absences and Snow Days as Disruptions of Instructional Time (National Bureau of Economic Research, 2014).

Birkelund, J. F. & Karlson, K. B. No evidence of a major learning slide 14 months into the COVID-19 pandemic in Denmark. European Societies https://doi.org/10.1080/14616696.2022.2129085 (2022).

Angrist, N., Djankov, S., Goldberg, P. K. & Patrinos, H. A. Measuring human capital using global learning data. Nature 592 , 403–408 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Torche, F. in Social Mobility in Developing Countries: Concepts, Methods, and Determinants (eds Iversen, V., Krishna, A. & Sen, K.) 139–171 (Oxford Univ. Press, 2021).

World Development Report 2018: Learning to Realize Education’s Promise (World Bank, 2018).

Policy Brief: Education during COVID-19 and Beyond (United Nations, 2020).

One Year into COVID-19 Education Disruption: Where Do We Stand? (UNESCO, 2021).

Azevedo, J. P., Hasan, A., Goldemberg, D., Geven, K. & Iqbal, S. A. Simulating the potential impacts of COVID-19 school closures on schooling and learning outcomes: a set of global estimates. World Bank Res. Observer 36 , 1–40 (2021).

Google Scholar  

Ardington, C., Wills, G. & Kotze, J. COVID-19 learning losses: early grade reading in South Africa. Int. J. Educ. Dev. 86 , 102480 (2021).

Hevia, F. J., Vergara-Lope, S., Velásquez-Durán, A. & Calderón, D. Estimation of the fundamental learning loss and learning poverty related to COVID-19 pandemic in Mexico. Int. J. Educ. Dev. 88 , 102515 (2022).

Lichand, G., Doria, C. A., Leal-Neto, O. & Fernandes, J. P. C. The impacts of remote learning in secondary education during the pandemic in Brazil. Nat. Hum. Behav. 6 , 1079–1086 (2022).

Major, L. E., Eyles, A., Machin, S. et al. Learning Loss since Lockdown: Variation across the Home Nations (Centre for Economic Performance, London School of Economics and Political Science, 2021).

Di Pietro, G., Biagi, F., Costa, P., Karpinski, Z. & Mazza, J. The Likely Impact of COVID-19 on Education: Reflections Based on the Existing Literature and Recent International Datasets (Publications Office of the European Union, 2020).

Fuchs-Schündeln, N., Krueger, D., Ludwig, A. & Popova, I. The Long-Term Distributional and Welfare Effects of COVID-19 School Closures (National Bureau of Economic Research, 2020).

Kaffenberger, M. Modelling the long-run learning impact of the COVID-19 learning shock: actions to (more than) mitigate loss. Int. J. Educ. Dev. 81 , 102326 (2021).

Attewell, P. & Newman, K. S. Growing Gaps: Educational Inequality around the World (Oxford Univ. Press, 2010).

Betthäuser, B. A., Kaiser, C. & Trinh, N. A. Regional variation in inequality of educational opportunity across europe. Socius https://doi.org/10.1177/23780231211019890 (2021).

Angrist, N. et al. Building back better to avert a learning catastrophe: estimating learning loss from covid-19 school shutdowns in africa and facilitating short-term and long-term learning recovery. Int. J. Educ. Dev. 84 , 102397 (2021).

Conley, D. & Johnson, T. Opinion: Past is future for the era of COVID-19 research in the social sciences. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2104155118 (2021).

Angrist, N., Bergman, P. & Matsheng, M. Experimental evidence on learning using low-tech when school is out. Nat. Hum. Behav. 6 , 941–950 (2022).

Lichand, G., Christen, J. & van Egeraat, E. Do Behavioral Nudges Work under Remote Learning? Evidence from Brazil during the Pandemic (Univ. Zurich, 2022).

Ouzzani, M., Hammady, H., Fedorowicz, Z. & Elmagarmid, A. Rayyan—a web and mobile app for systematic reviews. Syst. Rev. 5 , 1–10 (2016).

Tomasik, M. J., Helbling, L. A. & Moser, U. Educational gains of in-person vs. distance learning in primary and secondary schools: a natural experiment during the COVID-19 pandemic school closures in Switzerland. Int. J. Psychol. 56 , 566–576 (2021).

Eurybase: The Information Database on Education Systems in Europe (Eurydice, 2021).

Borenstein, M., Hedges, L. V., Higgins, J. P. & Rothstein, H. R. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Methods 1 , 97–111 (2010).

Ogilvie, D. et al. The harvest plot: a method for synthesising evidence about the differential effects of interventions. BMC Med. Res. Methodol. 8 , 1–7 (2008).

Gore, J., Fray, L., Miller, A., Harris, J. & Taggart, W. The impact of COVID-19 on student learning in New South Wales primary schools: an empirical study. Aust. Educ. Res. 48 , 605–637 (2021).

Gambi, L. & De Witte, K. The Resiliency of School Outcomes after the COVID-19 Pandemic: Standardised Test Scores and Inequality One Year after Long Term School Closures (FEB Research Report Department of Economics, 2021).

Maldonado, J. E. & De Witte, K. The effect of school closures on standardised student test outcomes. Br. Educ. Res. J. 48 , 49–94 (2021).

Vegas, E. COVID-19’s Impact on Learning Losses and Learning Inequality in Colombia (Center for Universal Education at Brookings, 2022).

Depping, D., Lücken, M., Musekamp, F. & Thonke, F. in Schule während der Corona-Pandemie. Neue Ergebnisse und Überblick über ein dynamisches Forschungsfeld (eds Fickermann, D. & Edelstein, B.) 51–79 (Münster & New York: Waxmann, 2021).

Ludewig, U. et al. Die COVID-19 Pandemie und Lesekompetenz von Viertklässler*innen: Ergebnisse der IFS-Schulpanelstudie 2016–2021 (Institut für Schulentwicklungsforschung, Univ. Dortmund, 2022).

Schult, J., Mahler, N., Fauth, B. & Lindner, M. A. Did students learn less during the COVID-19 pandemic? Reading and mathematics competencies before and after the first pandemic wave. Sch. Eff. Sch. Improv. https://doi.org/10.1080/09243453.2022.2061014 (2022).

Schult, J., Mahler, N., Fauth, B. & Lindner, M. A. Long-term consequences of repeated school closures during the COVID-19 pandemic for reading and mathematics competencies. Front. Educ. https://doi.org/10.3389/feduc.2022.867316 (2022).

Bazoli, N., Marzadro, S., Schizzerotto, A. & Vergolini, L. Learning Loss and Students’ Social Origins during the COVID-19 Pandemic in Italy (FBK-IRVAPP Working Papers 3, 2022).

Borgonovi, F. & Ferrara, A. The effects of COVID-19 on inequalities in educational achievement in Italy. Preprint at SSRN https://doi.org/10.2139/ssrn.4171968 (2022).

Contini, D., Di Tommaso, M. L., Muratori, C., Piazzalunga, D. & Schiavon, L. Who lost the most? Mathematics achievement during the COVID-19 pandemic. BE J. Econ. Anal. Policy 22 , 399–408 (2022).

Engzell, P., Frey, A. & Verhagen, M. D. Learning loss due to school closures during the COVID-19 pandemic. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2022376118 (2021).

Haelermans, C. Learning Growth and Inequality in Primary Education: Policy Lessons from the COVID-19 Crisis (The European Liberal Forum (ELF)-FORES, 2021).

Haelermans, C. et al. A Full Year COVID-19 Crisis with Interrupted Learning and Two School Closures: The Effects on Learning Growth and Inequality in Primary Education (Maastricht Univ., Research Centre for Education and the Labour Market (ROA), 2021).

Haelermans, C. et al. Sharp increase in inequality in education in times of the COVID-19-pandemic. PLoS ONE 17 , e0261114 (2022).

Schuurman, T. M., Henrichs, L. F., Schuurman, N. K., Polderdijk, S. & Hornstra, L. Learning loss in vulnerable student populations after the first COVID-19 school closure in the Netherlands. Scand. J. Educ. Res. https://doi.org/10.1080/00313831.2021.2006307 (2021).

Arenas, A. & Gortazar, L. Learning Loss One Year after School Closures (Esade Working Paper, 2022).

Hallin, A. E., Danielsson, H., Nordström, T. & Fälth, L. No learning loss in Sweden during the pandemic evidence from primary school reading assessments. Int. J. Educ. Res. 114 , 102011 (2022).

Blainey, K. & Hannay, T. The Impact of School Closures on Autumn 2020 Attainment (RS Assessment from Hodder Education and SchoolDash, 2021).

Blainey, K. & Hannay, T. The Impact of School Closures on Spring 2021 Attainment (RS Assessment from Hodder Education and SchoolDash, 2021).

Blainey, K. & Hannay, T. The Effects of Educational Disruption on Primary School Attainment in Summer 2021 (RS Assessment from Hodder Education and SchoolDash, 2021).

Understanding Progress in the 2020/21 Academic Year: Complete Findings from the Autumn Term (London: Department for Education, 2021).

Understanding Progress in the 2020/21 Academic Year: Initial Findings from the Spring Term (London: Department for Education, 2021).

Impact of COVID-19 on Attainment: Initial Analysis (Brentford: GL Assessment, 2021).

Rose, S. et al. Impact of School Closures and Subsequent Support Strategies on Attainment and Socio-emotional Wellbeing in Key Stage 1: Interim Paper 1 (National Foundation for Educational Research (NFER) and Education Endowment Foundation (EEF) , 2021).

Rose, S. et al. Impact of School Closures and Subsequent Support Strategies on Attainment and Socio-emotional Wellbeing in Key Stage 1: Interim Paper 2 (National Foundation for Educational Research (NFER) and Education Endowment Foundation (EEF), 2021).

Weidmann, B. et al. COVID-19 Disruptions: Attainment Gaps and Primary School Responses (Education Endowment Foundation, 2021).

Bielinski, J., Brown, R. & Wagner, K. No Longer a Prediction: What New Data Tell Us About the Effects of 2020 Learning Disruptions (Illuminate Education, 2021).

Domingue, B. W., Hough, H. J., Lang, D. & Yeatman, J. Changing Patterns of Growth in Oral Reading Fluency During the COVID-19 Pandemic. PACE Working Paper (Policy Analysis for California Education, 2021).

Domingue, B. et al. The effect of COVID on oral reading fluency during the 2020–2021 academic year. AERA Open https://doi.org/10.1177/23328584221120254 (2022).

Kogan, V. & Lavertu, S. The COVID-19 Pandemic and Student Achievement on Ohio’s Third-Grade English Language Arts Assessment (Ohio State Univ., 2021).

Kogan, V. & Lavertu, S. How the COVID-19 Pandemic Affected Student Learning in Ohio: Analysis of Spring 2021 Ohio State Tests (Ohio State Univ., 2021).

Kozakowski, W., Gill, B., Lavallee, P., Burnett, A. & Ladinsky, J. Changes in Academic Achievement in Pittsburgh Public Schools during Remote Instruction in the COVID-19 Pandemic (Institute of Education Sciences (IES), US Department of Education, 2020).

Kuhfeld, M. & Lewis, K. Student Achievement in 2021–2022: Cause for Hope and Continued Urgency (NWEA, 2022).

Lewis, K., Kuhfeld, M., Ruzek, E. & McEachin, A. Learning during COVID-19: Reading and Math Achievement in the 2020–21 School Year (NWEA, 2021).

Locke, V. N., Patarapichayatham, C. & Lewis, S. Learning Loss in Reading and Math in US Schools Due to the COVID-19 Pandemic (Istation, 2021).

Pier, L., Christian, M., Tymeson, H. & Meyer, R. H. COVID-19 Impacts on Student Learning: Evidence from Interim Assessments in California. PACE Working Paper (Policy Analysis for California Education, 2021).

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Acknowledgements

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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Betthäuser, B.A., Bach-Mortensen, A.M. & Engzell, P. A systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic. Nat Hum Behav 7 , 375–385 (2023). https://doi.org/10.1038/s41562-022-01506-4

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About half of americans say public k-12 education is going in the wrong direction.

About half of U.S. adults (51%) say the country’s public K-12 education system is generally going in the wrong direction. A far smaller share (16%) say it’s going in the right direction, and about a third (32%) are not sure, according to a Pew Research Center survey conducted in November 2023.

Pew Research Center conducted this analysis to understand how Americans view the K-12 public education system. We surveyed 5,029 U.S. adults from Nov. 9 to Nov. 16, 2023.

The survey was conducted by Ipsos for Pew Research Center on the Ipsos KnowledgePanel Omnibus. The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey is weighted by gender, age, race, ethnicity, education, income and other categories.

Here are the questions used for this analysis , along with responses, and the survey methodology .

A majority of those who say it’s headed in the wrong direction say a major reason is that schools are not spending enough time on core academic subjects.

These findings come amid debates about what is taught in schools , as well as concerns about school budget cuts and students falling behind academically.

Related: Race and LGBTQ Issues in K-12 Schools

Republicans are more likely than Democrats to say the public K-12 education system is going in the wrong direction. About two-thirds of Republicans and Republican-leaning independents (65%) say this, compared with 40% of Democrats and Democratic leaners. In turn, 23% of Democrats and 10% of Republicans say it’s headed in the right direction.

Among Republicans, conservatives are the most likely to say public education is headed in the wrong direction: 75% say this, compared with 52% of moderate or liberal Republicans. There are no significant differences among Democrats by ideology.

Similar shares of K-12 parents and adults who don’t have a child in K-12 schools say the system is going in the wrong direction.

A separate Center survey of public K-12 teachers found that 82% think the overall state of public K-12 education has gotten worse in the past five years. And many teachers are pessimistic about the future.

Related: What’s It Like To Be A Teacher in America Today?

Why do Americans think public K-12 education is going in the wrong direction?

We asked adults who say the public education system is going in the wrong direction why that might be. About half or more say the following are major reasons:

• Schools not spending enough time on core academic subjects, like reading, math, science and social studies (69%)
• Teachers bringing their personal political and social views into the classroom (54%)
• Schools not having the funding and resources they need (52%)

About a quarter (26%) say a major reason is that parents have too much influence in decisions about what schools are teaching.

How views vary by party

Americans in each party point to different reasons why public education is headed in the wrong direction.

Republicans are more likely than Democrats to say major reasons are:

• A lack of focus on core academic subjects (79% vs. 55%)
• Teachers bringing their personal views into the classroom (76% vs. 23%)

In turn, Democrats are more likely than Republicans to point to:

• Insufficient school funding and resources (78% vs. 33%)
• Parents having too much say in what schools are teaching (46% vs. 13%)

Views also vary within each party by ideology.

Among Republicans, conservatives are particularly likely to cite a lack of focus on core academic subjects and teachers bringing their personal views into the classroom.

Among Democrats, liberals are especially likely to cite schools lacking resources and parents having too much say in the curriculum.

Note: Here are the questions used for this analysis , along with responses, and the survey methodology .

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Fresh data delivered Saturday mornings

‘Back to school’ means anytime from late July to after Labor Day, depending on where in the U.S. you live

Among many u.s. children, reading for fun has become less common, federal data shows, most european students learn english in school, for u.s. teens today, summer means more schooling and less leisure time than in the past, about one-in-six u.s. teachers work second jobs – and not just in the summer, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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​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

Sarah Mervosh reports on K-12 education, and Francesca Paris is a data reporter.

In Anchorage, affluent families set off on ski trips and other lengthy vacations, with the assumption that their children can keep up with schoolwork online.

In a working-class pocket of Michigan, school administrators have tried almost everything, including pajama day, to boost student attendance.

And across the country, students with heightened anxiety are opting to stay home rather than face the classroom.

In the four years since the pandemic closed schools, U.S. education has struggled to recover on a number of fronts, from learning loss , to enrollment , to student behavior .

But perhaps no issue has been as stubborn and pervasive as a sharp increase in student absenteeism, a problem that cuts across demographics and has continued long after schools reopened.

Nationally, an estimated 26 percent of public school students were considered chronically absent last school year, up from 15 percent before the pandemic, according to the most recent data, from 40 states and Washington, D.C., compiled by the conservative-leaning American Enterprise Institute . Chronic absence is typically defined as missing at least 10 percent of the school year, or about 18 days, for any reason.

Source: Upshot analysis of data from Nat Malkus, American Enterprise Institute. Districts are grouped into highest, middle and lowest third.

The increases have occurred in districts big and small, and across income and race. For districts in wealthier areas, chronic absenteeism rates have about doubled, to 19 percent in the 2022-23 school year from 10 percent before the pandemic, a New York Times analysis of the data found.

Poor communities, which started with elevated rates of student absenteeism, are facing an even bigger crisis: Around 32 percent of students in the poorest districts were chronically absent in the 2022-23 school year, up from 19 percent before the pandemic.

Even districts that reopened quickly during the pandemic, in fall 2020, have seen vast increases.

“The problem got worse for everybody in the same proportional way,” said Nat Malkus, a senior fellow at the American Enterprise Institute, who collected and studied the data.

Victoria, Texas reopened schools in August 2020, earlier than many other districts. Even so, student absenteeism in the district has doubled.

Kaylee Greenlee for The New York Times

The trends suggest that something fundamental has shifted in American childhood and the culture of school, in ways that may be long lasting. What was once a deeply ingrained habit — wake up, catch the bus, report to class — is now something far more tenuous.

“Our relationship with school became optional,” said Katie Rosanbalm, a psychologist and associate research professor with the Center for Child and Family Policy at Duke University.

The habit of daily attendance — and many families’ trust — was severed when schools shuttered in spring 2020. Even after schools reopened, things hardly snapped back to normal. Districts offered remote options, required Covid-19 quarantines and relaxed policies around attendance and grading .

Source: Nat Malkus, American Enterprise Institute . Includes districts with at least 1,500 students in 2019. Numbers are rounded. U.S. average is estimated.

Today, student absenteeism is a leading factor hindering the nation’s recovery from pandemic learning losses , educational experts say. Students can’t learn if they aren’t in school. And a rotating cast of absent classmates can negatively affect the achievement of even students who do show up, because teachers must slow down and adjust their approach to keep everyone on track.

“If we don’t address the absenteeism, then all is naught,” said Adam Clark, the superintendent of Mt. Diablo Unified, a socioeconomically and racially diverse district of 29,000 students in Northern California, where he said absenteeism has “exploded” to about 25 percent of students. That’s up from 12 percent before the pandemic.

U.S. students, overall, are not caught up from their pandemic losses. Absenteeism is one key reason.

Why Students Are Missing School

Schools everywhere are scrambling to improve attendance, but the new calculus among families is complex and multifaceted.

At South Anchorage High School in Anchorage, where students are largely white and middle-to-upper income, some families now go on ski trips during the school year, or take advantage of off-peak travel deals to vacation for two weeks in Hawaii, said Sara Miller, a counselor at the school.

For a smaller number of students at the school who qualify for free or reduced-price lunch, the reasons are different, and more intractable. They often have to stay home to care for younger siblings, Ms. Miller said. On days they miss the bus, their parents are busy working or do not have a car to take them to school.

And because teachers are still expected to post class work online, often nothing more than a skeleton version of an assignment, families incorrectly think students are keeping up, Ms. Miller said.

Sara Miller, a counselor at South Anchorage High School for 20 years, now sees more absences from students across the socioeconomic spectrum.

Ash Adams for The New York Times

Across the country, students are staying home when sick , not only with Covid-19, but also with more routine colds and viruses.

And more students are struggling with their mental health, one reason for increased absenteeism in Mason, Ohio, an affluent suburb of Cincinnati, said Tracey Carson, a district spokeswoman. Because many parents can work remotely, their children can also stay home.

For Ashley Cooper, 31, of San Marcos, Texas, the pandemic fractured her trust in an education system that she said left her daughter to learn online, with little support, and then expected her to perform on grade level upon her return. Her daughter, who fell behind in math, has struggled with anxiety ever since, she said.

“There have been days where she’s been absolutely in tears — ‘Can’t do it. Mom, I don’t want to go,’” said Ms. Cooper, who has worked with the nonprofit Communities in Schools to improve her children’s school attendance. But she added, “as a mom, I feel like it’s OK to have a mental health day, to say, ‘I hear you and I listen. You are important.’”

Experts say missing school is both a symptom of pandemic-related challenges, and also a cause. Students who are behind academically may not want to attend, but being absent sets them further back. Anxious students may avoid school, but hiding out can fuel their anxiety.

And schools have also seen a rise in discipline problems since the pandemic, an issue intertwined with absenteeism.

Dr. Rosanbalm, the Duke psychologist, said both absenteeism and behavioral outbursts are examples of the human stress response, now playing out en masse in schools: fight (verbal or physical aggression) or flight (absenteeism).

“If kids are not here, they are not forming relationships,” said Quintin Shepherd, the superintendent in Victoria, Texas.

Quintin Shepherd, the superintendent in Victoria, Texas, first put his focus on student behavior, which he described as a “fire in the kitchen” after schools reopened in August 2020.

The district, which serves a mostly low-income and Hispanic student body of around 13,000, found success with a one-on-one coaching program that teaches coping strategies to the most disruptive students. In some cases, students went from having 20 classroom outbursts per year to fewer than five, Dr. Shepherd said.

But chronic absenteeism is yet to be conquered. About 30 percent of students are chronically absent this year, roughly double the rate before the pandemic.

Dr. Shepherd, who originally hoped student absenteeism would improve naturally with time, has begun to think that it is, in fact, at the root of many issues.

“If kids are not here, they are not forming relationships,” he said. “If they are not forming relationships, we should expect there will be behavior and discipline issues. If they are not here, they will not be academically learning and they will struggle. If they struggle with their coursework, you can expect violent behaviors.”

Teacher absences have also increased since the pandemic, and student absences mean less certainty about which friends and classmates will be there. That can lead to more absenteeism, said Michael A. Gottfried, a professor at the University of Pennsylvania Graduate School of Education. His research has found that when 10 percent of a student’s classmates are absent on a given day, that student is more likely to be absent the following day.

Absent classmates can have a negative impact on the achievement and attendance of even the students who do show up.

Is This the New Normal?

In many ways, the challenge facing schools is one felt more broadly in American society: Have the cultural shifts from the pandemic become permanent?

In the work force, U.S. employees are still working from home at a rate that has remained largely unchanged since late 2022 . Companies have managed to “put the genie back in the bottle” to some extent by requiring a return to office a few days a week, said Nicholas Bloom, an economist at Stanford University who studies remote work. But hybrid office culture, he said, appears here to stay.

Some wonder whether it is time for schools to be more pragmatic.

Lakisha Young, the chief executive of the Oakland REACH, a parent advocacy group that works with low-income families in California, suggested a rigorous online option that students could use in emergencies, such as when a student misses the bus or has to care for a family member. “The goal should be, how do I ensure this kid is educated?” she said.

Relationships with adults at school and other classmates are crucial for attendance.

In the corporate world, companies have found some success appealing to a sense of social responsibility, where colleagues rely on each other to show up on the agreed-upon days.

A similar dynamic may be at play in schools, where experts say strong relationships are critical for attendance.

There is a sense of: “If I don’t show up, would people even miss the fact that I’m not there?” said Charlene M. Russell-Tucker, the commissioner of education in Connecticut.

In her state, a home visit program has yielded positive results , in part by working with families to address the specific reasons a student is missing school, but also by establishing a relationship with a caring adult. Other efforts — such as sending text messages or postcards to parents informing them of the number of accumulated absences — can also be effective.

Regina Murff has worked to re-establish the daily habit of school attendance for her sons, who are 6 and 12.

Sylvia Jarrus for The New York Times

In Ypsilanti, Mich., outside of Ann Arbor, a home visit helped Regina Murff, 44, feel less alone when she was struggling to get her children to school each morning.

After working at a nursing home during the pandemic, and later losing her sister to Covid-19, she said, there were days she found it difficult to get out of bed. Ms. Murff was also more willing to keep her children home when they were sick, for fear of accidentally spreading the virus.

But after a visit from her school district, and starting therapy herself, she has settled into a new routine. She helps her sons, 6 and 12, set out their outfits at night and she wakes up at 6 a.m. to ensure they get on the bus. If they are sick, she said, she knows to call the absence into school. “I’ve done a huge turnaround in my life,” she said.

But bringing about meaningful change for large numbers of students remains slow, difficult work .

Nationally, about 26 percent of students were considered chronically absent last school year, up from 15 percent before the pandemic.

The Ypsilanti school district has tried a bit of everything, said the superintendent, Alena Zachery-Ross. In addition to door knocks, officials are looking for ways to make school more appealing for the district’s 3,800 students, including more than 80 percent who qualify for free or reduced-price lunch. They held themed dress-up days — ’70s day, pajama day — and gave away warm clothes after noticing a dip in attendance during winter months.

“We wondered, is it because you don’t have a coat, you don’t have boots?” said Dr. Zachery-Ross.

Still, absenteeism overall remains higher than it was before the pandemic. “We haven’t seen an answer,” she said.

Data provided by Nat Malkus, with the American Enterprise Institute. The data was originally published on the Return to Learn tracker and used for the report “ Long COVID for Public Schools: Chronic Absenteeism Before and After the Pandemic .”

The analysis for each year includes all districts with available data for that year, weighted by district size. Data are sourced from states, where available, and the U.S. Department of Education and NCES Common Core of Data.

For the 2018-19 school year, data was available for all 50 states and the District of Columbia. For 2022-23, it was available for 40 states and D.C., due to delays in state reporting.

Closure length status is based on the most in-person learning option available. Poverty is measured using the Census Bureau’s Small Area Income and Poverty Estimates. School size and minority population estimates are from NCES CCD.

How absenteeism is measured can vary state by state, which means comparisons across state lines may not be reliable.

An earlier version of this article misnamed a research center at Duke University. It is the Center for Child and Family Policy, not the Center of Child and Family Policy.

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Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

Stella timotheou.

1 CYENS Center of Excellence & Cyprus University of Technology (Cyprus Interaction Lab), Cyprus, CYENS Center of Excellence & Cyprus University of Technology, Nicosia-Limassol, Cyprus

Ourania Miliou

Yiannis dimitriadis.

2 Universidad de Valladolid (UVA), Spain, Valladolid, Spain

Sara Villagrá Sobrino

Nikoleta giannoutsou, romina cachia.

3 JRC - Joint Research Centre of the European Commission, Seville, Spain

Alejandra Martínez Monés

Andri ioannou, associated data.

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools’ digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students’ performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.

Introduction

Digital technologies have brought changes to the nature and scope of education. Versatile and disruptive technological innovations, such as smart devices, the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR) and virtual reality (VR), blockchain, and software applications have opened up new opportunities for advancing teaching and learning (Gaol & Prasolova-Førland, 2021 ; OECD, 2021 ). Hence, in recent years, education systems worldwide have increased their investment in the integration of information and communication technology (ICT) (Fernández-Gutiérrez et al., 2020 ; Lawrence & Tar, 2018 ) and prioritized their educational agendas to adapt strategies or policies around ICT integration (European Commission, 2019 ). The latter brought about issues regarding the quality of teaching and learning with ICTs (Bates, 2015 ), especially concerning the understanding, adaptation, and design of education systems in accordance with current technological trends (Balyer & Öz, 2018 ). Studies have shown that despite the investment made in the integration of technology in schools, the results have not been promising, and the intended outcomes have not yet been achieved (Delgado et al., 2015 ; Lawrence & Tar, 2018 ). These issues were exacerbated during the COVID-19 pandemic, which forced teaching across education levels to move online (Daniel, 2020 ). Online teaching accelerated the use of digital technologies generating questions regarding the process, the nature, the extent, and the effectiveness of digitalization in schools (Cachia et al., 2021 ; König et al., 2020 ). Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses (Blaskó et al., 2021 ; Di Pietro et al, 2020 ). Such results have engendered the need for schools to learn and build upon the experience in order to enhance their digital capacity (European Commission, 2020 ) and increase their digitalization levels (Costa et al., 2021 ). Digitalization offers possibilities for fundamental improvement in schools (OECD, 2021 ; Rott & Marouane, 2018 ) and touches many aspects of a school’s development (Delcker & Ifenthaler, 2021 ) . However, it is a complex process that requires large-scale transformative changes beyond the technical aspects of technology and infrastructure (Pettersson, 2021 ). Namely, digitalization refers to “ a series of deep and coordinated culture, workforce, and technology shifts and operating models ” (Brooks & McCormack, 2020 , p. 3) that brings cultural, organizational, and operational change through the integration of digital technologies (JISC, 2020 ). A successful digital transformation requires that schools increase their digital capacity levels, establishing the necessary “ culture, policies, infrastructure as well as digital competence of students and staff to support the effective integration of technology in teaching and learning practices ” (Costa et al, 2021 , p.163).

Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem (Eng, 2005 ), there is a need to show how the different elements of the impact are interconnected and to identify the factors that can encourage an effective and efficient change in the school environment. To address the issues outlined above, we formulated the following research questions:

a) What is the impact of digital technologies on education?

b) Which factors might affect a school’s digital capacity and transformation?

In the present investigation, we conducted a non-systematic literature review of publications pertaining to the impact of digital technologies on education and the factors that affect a school’s digital capacity and transformation. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors which affect the schools’ digital capacity and digital transformation.

Methodology

The non-systematic literature review presented herein covers the main theories and research published over the past 17 years on the topic. It is based on meta-analyses and review papers found in scholarly, peer-reviewed content databases and other key studies and reports related to the concepts studied (e.g., digitalization, digital capacity) from professional and international bodies (e.g., the OECD). We searched the Scopus database, which indexes various online journals in the education sector with an international scope, to collect peer-reviewed academic papers. Furthermore, we used an all-inclusive Google Scholar search to include relevant key terms or to include studies found in the reference list of the peer-reviewed papers, and other key studies and reports related to the concepts studied by professional and international bodies. Lastly, we gathered sources from the Publications Office of the European Union ( https://op.europa.eu/en/home ); namely, documents that refer to policies related to digital transformation in education.

Regarding search terms, we first searched resources on the impact of digital technologies on education by performing the following search queries: “impact” OR “effects” AND “digital technologies” AND “education”, “impact” OR “effects” AND “ICT” AND “education”. We further refined our results by adding the terms “meta-analysis” and “review” or by adjusting the search options based on the features of each database to avoid collecting individual studies that would provide limited contributions to a particular domain. We relied on meta-analyses and review studies as these consider the findings of multiple studies to offer a more comprehensive view of the research in a given area (Schuele & Justice, 2006 ). Specifically, meta-analysis studies provided quantitative evidence based on statistically verifiable results regarding the impact of educational interventions that integrate digital technologies in school classrooms (Higgins et al., 2012 ; Tolani-Brown et al., 2011 ).

However, quantitative data does not offer explanations for the challenges or difficulties experienced during ICT integration in learning and teaching (Tolani-Brown et al., 2011 ). To fill this gap, we analyzed literature reviews and gathered in-depth qualitative evidence of the benefits and implications of technology integration in schools. In the analysis presented herein, we also included policy documents and reports from professional and international bodies and governmental reports, which offered useful explanations of the key concepts of this study and provided recent evidence on digital capacity and transformation in education along with policy recommendations. The inclusion and exclusion criteria that were considered in this study are presented in Table ​ Table1 1 .

Inclusion and exclusion criteria for the selection of resources on the impact of digital technologies on education

To ensure a reliable extraction of information from each study and assist the research synthesis we selected the study characteristics of interest (impact) and constructed coding forms. First, an overview of the synthesis was provided by the principal investigator who described the processes of coding, data entry, and data management. The coders followed the same set of instructions but worked independently. To ensure a common understanding of the process between coders, a sample of ten studies was tested. The results were compared, and the discrepancies were identified and resolved. Additionally, to ensure an efficient coding process, all coders participated in group meetings to discuss additions, deletions, and modifications (Stock, 1994 ). Due to the methodological diversity of the studied documents we began to synthesize the literature review findings based on similar study designs. Specifically, most of the meta-analysis studies were grouped in one category due to the quantitative nature of the measured impact. These studies tended to refer to student achievement (Hattie et al., 2014 ). Then, we organized the themes of the qualitative studies in several impact categories. Lastly, we synthesized both review and meta-analysis data across the categories. In order to establish a collective understanding of the concept of impact, we referred to a previous impact study by Balanskat ( 2009 ) which investigated the impact of technology in primary schools. In this context, the impact had a more specific ICT-related meaning and was described as “ a significant influence or effect of ICT on the measured or perceived quality of (parts of) education ” (Balanskat, 2009 , p. 9). In the study presented herein, the main impacts are in relation to learning and learners, teaching, and teachers, as well as other key stakeholders who are directly or indirectly connected to the school unit.

The study’s results identified multiple dimensions of the impact of digital technologies on students’ knowledge, skills, and attitudes; on equality, inclusion, and social integration; on teachers’ professional and teaching practices; and on other school-related aspects and stakeholders. The data analysis indicated various factors that might affect the schools’ digital capacity and transformation, such as digital competencies, the teachers’ personal characteristics and professional development, as well as the school’s leadership and management, administration, infrastructure, etc. The impacts and factors found in the literature review are presented below.

Impacts of digital technologies on students’ knowledge, skills, attitudes, and emotions

The impact of ICT use on students’ knowledge, skills, and attitudes has been investigated early in the literature. Eng ( 2005 ) found a small positive effect between ICT use and students' learning. Specifically, the author reported that access to computer-assisted instruction (CAI) programs in simulation or tutorial modes—used to supplement rather than substitute instruction – could enhance student learning. The author reported studies showing that teachers acknowledged the benefits of ICT on pupils with special educational needs; however, the impact of ICT on students' attainment was unclear. Balanskat et al. ( 2006 ) found a statistically significant positive association between ICT use and higher student achievement in primary and secondary education. The authors also reported improvements in the performance of low-achieving pupils. The use of ICT resulted in further positive gains for students, namely increased attention, engagement, motivation, communication and process skills, teamwork, and gains related to their behaviour towards learning. Evidence from qualitative studies showed that teachers, students, and parents recognized the positive impact of ICT on students' learning regardless of their competence level (strong/weak students). Punie et al. ( 2006 ) documented studies that showed positive results of ICT-based learning for supporting low-achieving pupils and young people with complex lives outside the education system. Liao et al. ( 2007 ) reported moderate positive effects of computer application instruction (CAI, computer simulations, and web-based learning) over traditional instruction on primary school student's achievement. Similarly, Tamim et al. ( 2011 ) reported small to moderate positive effects between the use of computer technology (CAI, ICT, simulations, computer-based instruction, digital and hypermedia) and student achievement in formal face-to-face classrooms compared to classrooms that did not use technology. Jewitt et al., ( 2011 ) found that the use of learning platforms (LPs) (virtual learning environments, management information systems, communication technologies, and information- and resource-sharing technologies) in schools allowed primary and secondary students to access a wider variety of quality learning resources, engage in independent and personalized learning, and conduct self- and peer-review; LPs also provide opportunities for teacher assessment and feedback. Similar findings were reported by Fu ( 2013 ), who documented a list of benefits and opportunities of ICT use. According to the author, the use of ICTs helps students access digital information and course content effectively and efficiently, supports student-centered and self-directed learning, as well as the development of a creative learning environment where more opportunities for critical thinking skills are offered, and promotes collaborative learning in a distance-learning environment. Higgins et al. ( 2012 ) found consistent but small positive associations between the use of technology and learning outcomes of school-age learners (5–18-year-olds) in studies linking the provision and use of technology with attainment. Additionally, Chauhan ( 2017 ) reported a medium positive effect of technology on the learning effectiveness of primary school students compared to students who followed traditional learning instruction.

The rise of mobile technologies and hardware devices instigated investigations into their impact on teaching and learning. Sung et al. ( 2016 ) reported a moderate effect on students' performance from the use of mobile devices in the classroom compared to the use of desktop computers or the non-use of mobile devices. Schmid et al. ( 2014 ) reported medium–low to low positive effects of technology integration (e.g., CAI, ICTs) in the classroom on students' achievement and attitude compared to not using technology or using technology to varying degrees. Tamim et al. ( 2015 ) found a low statistically significant effect of the use of tablets and other smart devices in educational contexts on students' achievement outcomes. The authors suggested that tablets offered additional advantages to students; namely, they reported improvements in students’ notetaking, organizational and communication skills, and creativity. Zheng et al. ( 2016 ) reported a small positive effect of one-to-one laptop programs on students’ academic achievement across subject areas. Additional reported benefits included student-centered, individualized, and project-based learning enhanced learner engagement and enthusiasm. Additionally, the authors found that students using one-to-one laptop programs tended to use technology more frequently than in non-laptop classrooms, and as a result, they developed a range of skills (e.g., information skills, media skills, technology skills, organizational skills). Haßler et al. ( 2016 ) found that most interventions that included the use of tablets across the curriculum reported positive learning outcomes. However, from 23 studies, five reported no differences, and two reported a negative effect on students' learning outcomes. Similar results were indicated by Kalati and Kim ( 2022 ) who investigated the effect of touchscreen technologies on young students’ learning. Specifically, from 53 studies, 34 advocated positive effects of touchscreen devices on children’s learning, 17 obtained mixed findings and two studies reported negative effects.

More recently, approaches that refer to the impact of gamification with the use of digital technologies on teaching and learning were also explored. A review by Pan et al. ( 2022 ) that examined the role of learning games in fostering mathematics education in K-12 settings, reported that gameplay improved students’ performance. Integration of digital games in teaching was also found as a promising pedagogical practice in STEM education that could lead to increased learning gains (Martinez et al., 2022 ; Wang et al., 2022 ). However, although Talan et al. ( 2020 ) reported a medium effect of the use of educational games (both digital and non-digital) on academic achievement, the effect of non-digital games was higher.

Over the last two years, the effects of more advanced technologies on teaching and learning were also investigated. Garzón and Acevedo ( 2019 ) found that AR applications had a medium effect on students' learning outcomes compared to traditional lectures. Similarly, Garzón et al. ( 2020 ) showed that AR had a medium impact on students' learning gains. VR applications integrated into various subjects were also found to have a moderate effect on students’ learning compared to control conditions (traditional classes, e.g., lectures, textbooks, and multimedia use, e.g., images, videos, animation, CAI) (Chen et al., 2022b ). Villena-Taranilla et al. ( 2022 ) noted the moderate effect of VR technologies on students’ learning when these were applied in STEM disciplines. In the same meta-analysis, Villena-Taranilla et al. ( 2022 ) highlighted the role of immersive VR, since its effect on students’ learning was greater (at a high level) across educational levels (K-6) compared to semi-immersive and non-immersive integrations. In another meta-analysis study, the effect size of the immersive VR was small and significantly differentiated across educational levels (Coban et al., 2022 ). The impact of AI on education was investigated by Su and Yang ( 2022 ) and Su et al. ( 2022 ), who showed that this technology significantly improved students’ understanding of AI computer science and machine learning concepts.

It is worth noting that the vast majority of studies referred to learning gains in specific subjects. Specifically, several studies examined the impact of digital technologies on students’ literacy skills and reported positive effects on language learning (Balanskat et al., 2006 ; Grgurović et al., 2013 ; Friedel et al., 2013 ; Zheng et al., 2016 ; Chen et al., 2022b ; Savva et al., 2022 ). Also, several studies documented positive effects on specific language learning areas, namely foreign language learning (Kao, 2014 ), writing (Higgins et al., 2012 ; Wen & Walters, 2022 ; Zheng et al., 2016 ), as well as reading and comprehension (Cheung & Slavin, 2011 ; Liao et al., 2007 ; Schwabe et al., 2022 ). ICTs were also found to have a positive impact on students' performance in STEM (science, technology, engineering, and mathematics) disciplines (Arztmann et al., 2022 ; Bado, 2022 ; Villena-Taranilla et al., 2022 ; Wang et al., 2022 ). Specifically, a number of studies reported positive impacts on students’ achievement in mathematics (Balanskat et al., 2006 ; Hillmayr et al., 2020 ; Li & Ma, 2010 ; Pan et al., 2022 ; Ran et al., 2022 ; Verschaffel et al., 2019 ; Zheng et al., 2016 ). Furthermore, studies documented positive effects of ICTs on science learning (Balanskat et al., 2006 ; Liao et al., 2007 ; Zheng et al., 2016 ; Hillmayr et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ; Lei et al., 2022a ). Çelik ( 2022 ) also noted that computer simulations can help students understand learning concepts related to science. Furthermore, some studies documented that the use of ICTs had a positive impact on students’ achievement in other subjects, such as geography, history, music, and arts (Chauhan, 2017 ; Condie & Munro, 2007 ), and design and technology (Balanskat et al., 2006 ).

More specific positive learning gains were reported in a number of skills, e.g., problem-solving skills and pattern exploration skills (Higgins et al., 2012 ), metacognitive learning outcomes (Verschaffel et al., 2019 ), literacy skills, computational thinking skills, emotion control skills, and collaborative inquiry skills (Lu et al., 2022 ; Su & Yang, 2022 ; Su et al., 2022 ). Additionally, several investigations have reported benefits from the use of ICT on students’ creativity (Fielding & Murcia, 2022 ; Liu et al., 2022 ; Quah & Ng, 2022 ). Lastly, digital technologies were also found to be beneficial for enhancing students’ lifelong learning skills (Haleem et al., 2022 ).

Apart from gaining knowledge and skills, studies also reported improvement in motivation and interest in mathematics (Higgins et. al., 2019 ; Fadda et al., 2022 ) and increased positive achievement emotions towards several subjects during interventions using educational games (Lei et al., 2022a ). Chen et al. ( 2022a ) also reported a small but positive effect of digital health approaches in bullying and cyberbullying interventions with K-12 students, demonstrating that technology-based approaches can help reduce bullying and related consequences by providing emotional support, empowerment, and change of attitude. In their meta-review study, Su et al. ( 2022 ) also documented that AI technologies effectively strengthened students’ attitudes towards learning. In another meta-analysis, Arztmann et al. ( 2022 ) reported positive effects of digital games on motivation and behaviour towards STEM subjects.

Impacts of digital technologies on equality, inclusion and social integration

Although most of the reviewed studies focused on the impact of ICTs on students’ knowledge, skills, and attitudes, reports were also made on other aspects in the school context, such as equality, inclusion, and social integration. Condie and Munro ( 2007 ) documented research interventions investigating how ICT can support pupils with additional or special educational needs. While those interventions were relatively small scale and mostly based on qualitative data, their findings indicated that the use of ICTs enabled the development of communication, participation, and self-esteem. A recent meta-analysis (Baragash et al., 2022 ) with 119 participants with different disabilities, reported a significant overall effect size of AR on their functional skills acquisition. Koh’s meta-analysis ( 2022 ) also revealed that students with intellectual and developmental disabilities improved their competence and performance when they used digital games in the lessons.

Istenic Starcic and Bagon ( 2014 ) found that the role of ICT in inclusion and the design of pedagogical and technological interventions was not sufficiently explored in educational interventions with people with special needs; however, some benefits of ICT use were found in students’ social integration. The issue of gender and technology use was mentioned in a small number of studies. Zheng et al. ( 2016 ) reported a statistically significant positive interaction between one-to-one laptop programs and gender. Specifically, the results showed that girls and boys alike benefitted from the laptop program, but the effect on girls’ achievement was smaller than that on boys’. Along the same lines, Arztmann et al. ( 2022 ) reported no difference in the impact of game-based learning between boys and girls, arguing that boys and girls equally benefited from game-based interventions in STEM domains. However, results from a systematic review by Cussó-Calabuig et al. ( 2018 ) found limited and low-quality evidence on the effects of intensive use of computers on gender differences in computer anxiety, self-efficacy, and self-confidence. Based on their view, intensive use of computers can reduce gender differences in some areas and not in others, depending on contextual and implementation factors.

Impacts of digital technologies on teachers’ professional and teaching practices

Various research studies have explored the impact of ICT on teachers’ instructional practices and student assessment. Friedel et al. ( 2013 ) found that the use of mobile devices by students enabled teachers to successfully deliver content (e.g., mobile serious games), provide scaffolding, and facilitate synchronous collaborative learning. The integration of digital games in teaching and learning activities also gave teachers the opportunity to study and apply various pedagogical practices (Bado, 2022 ). Specifically, Bado ( 2022 ) found that teachers who implemented instructional activities in three stages (pre-game, game, and post-game) maximized students’ learning outcomes and engagement. For instance, during the pre-game stage, teachers focused on lectures and gameplay training, at the game stage teachers provided scaffolding on content, addressed technical issues, and managed the classroom activities. During the post-game stage, teachers organized activities for debriefing to ensure that the gameplay had indeed enhanced students’ learning outcomes.

Furthermore, ICT can increase efficiency in lesson planning and preparation by offering possibilities for a more collaborative approach among teachers. The sharing of curriculum plans and the analysis of students’ data led to clearer target settings and improvements in reporting to parents (Balanskat et al., 2006 ).

Additionally, the use and application of digital technologies in teaching and learning were found to enhance teachers’ digital competence. Balanskat et al. ( 2006 ) documented studies that revealed that the use of digital technologies in education had a positive effect on teachers’ basic ICT skills. The greatest impact was found on teachers with enough experience in integrating ICTs in their teaching and/or who had recently participated in development courses for the pedagogical use of technologies in teaching. Punie et al. ( 2006 ) reported that the provision of fully equipped multimedia portable computers and the development of online teacher communities had positive impacts on teachers’ confidence and competence in the use of ICTs.

Moreover, online assessment via ICTs benefits instruction. In particular, online assessments support the digitalization of students’ work and related logistics, allow teachers to gather immediate feedback and readjust to new objectives, and support the improvement of the technical quality of tests by providing more accurate results. Additionally, the capabilities of ICTs (e.g., interactive media, simulations) create new potential methods of testing specific skills, such as problem-solving and problem-processing skills, meta-cognitive skills, creativity and communication skills, and the ability to work productively in groups (Punie et al., 2006 ).

Impacts of digital technologies on other school-related aspects and stakeholders

There is evidence that the effective use of ICTs and the data transmission offered by broadband connections help improve administration (Balanskat et al., 2006 ). Specifically, ICTs have been found to provide better management systems to schools that have data gathering procedures in place. Condie and Munro ( 2007 ) reported impacts from the use of ICTs in schools in the following areas: attendance monitoring, assessment records, reporting to parents, financial management, creation of repositories for learning resources, and sharing of information amongst staff. Such data can be used strategically for self-evaluation and monitoring purposes which in turn can result in school improvements. Additionally, they reported that online access to other people with similar roles helped to reduce headteachers’ isolation by offering them opportunities to share insights into the use of ICT in learning and teaching and how it could be used to support school improvement. Furthermore, ICTs provided more efficient and successful examination management procedures, namely less time-consuming reporting processes compared to paper-based examinations and smooth communications between schools and examination authorities through electronic data exchange (Punie et al., 2006 ).

Zheng et al. ( 2016 ) reported that the use of ICTs improved home-school relationships. Additionally, Escueta et al. ( 2017 ) reported several ICT programs that had improved the flow of information from the school to parents. Particularly, they documented that the use of ICTs (learning management systems, emails, dedicated websites, mobile phones) allowed for personalized and customized information exchange between schools and parents, such as attendance records, upcoming class assignments, school events, and students’ grades, which generated positive results on students’ learning outcomes and attainment. Such information exchange between schools and families prompted parents to encourage their children to put more effort into their schoolwork.

The above findings suggest that the impact of ICT integration in schools goes beyond students’ performance in school subjects. Specifically, it affects a number of school-related aspects, such as equality and social integration, professional and teaching practices, and diverse stakeholders. In Table ​ Table2, 2 , we summarize the different impacts of digital technologies on school stakeholders based on the literature review, while in Table ​ Table3 3 we organized the tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript.

The impact of digital technologies on schools’ stakeholders based on the literature review

Tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript

Additionally, based on the results of the literature review, there are many types of digital technologies with different affordances (see, for example, studies on VR vs Immersive VR), which evolve over time (e.g. starting from CAIs in 2005 to Augmented and Virtual reality 2020). Furthermore, these technologies are linked to different pedagogies and policy initiatives, which are critical factors in the study of impact. Table ​ Table3 3 summarizes the different tools and practices that have been used to examine the impact of digital technologies on education since 2005 based on the review results.

Factors that affect the integration of digital technologies

Although the analysis of the literature review demonstrated different impacts of the use of digital technology on education, several authors highlighted the importance of various factors, besides the technology itself, that affect this impact. For example, Liao et al. ( 2007 ) suggested that future studies should carefully investigate which factors contribute to positive outcomes by clarifying the exact relationship between computer applications and learning. Additionally, Haßler et al., ( 2016 ) suggested that the neutral findings regarding the impact of tablets on students learning outcomes in some of the studies included in their review should encourage educators, school leaders, and school officials to further investigate the potential of such devices in teaching and learning. Several other researchers suggested that a number of variables play a significant role in the impact of ICTs on students’ learning that could be attributed to the school context, teaching practices and professional development, the curriculum, and learners’ characteristics (Underwood, 2009 ; Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Tang et al., 2022 ).

Digital competencies

One of the most common challenges reported in studies that utilized digital tools in the classroom was the lack of students’ skills on how to use them. Fu ( 2013 ) found that students’ lack of technical skills is a barrier to the effective use of ICT in the classroom. Tamim et al. ( 2015 ) reported that students faced challenges when using tablets and smart mobile devices, associated with the technical issues or expertise needed for their use and the distracting nature of the devices and highlighted the need for teachers’ professional development. Higgins et al. ( 2012 ) reported that skills training about the use of digital technologies is essential for learners to fully exploit the benefits of instruction.

Delgado et al. ( 2015 ), meanwhile, reported studies that showed a strong positive association between teachers’ computer skills and students’ use of computers. Teachers’ lack of ICT skills and familiarization with technologies can become a constraint to the effective use of technology in the classroom (Balanskat et al., 2006 ; Delgado et al., 2015 ).

It is worth noting that the way teachers are introduced to ICTs affects the impact of digital technologies on education. Previous studies have shown that teachers may avoid using digital technologies due to limited digital skills (Balanskat, 2006 ), or they prefer applying “safe” technologies, namely technologies that their own teachers used and with which they are familiar (Condie & Munro, 2007 ). In this regard, the provision of digital skills training and exposure to new digital tools might encourage teachers to apply various technologies in their lessons (Condie & Munro, 2007 ). Apart from digital competence, technical support in the school setting has also been shown to affect teachers’ use of technology in their classrooms (Delgado et al., 2015 ). Ferrari et al. ( 2011 ) found that while teachers’ use of ICT is high, 75% stated that they needed more institutional support and a shift in the mindset of educational actors to achieve more innovative teaching practices. The provision of support can reduce time and effort as well as cognitive constraints, which could cause limited ICT integration in the school lessons by teachers (Escueta et al., 2017 ).

Teachers’ personal characteristics, training approaches, and professional development

Teachers’ personal characteristics and professional development affect the impact of digital technologies on education. Specifically, Cheok and Wong ( 2015 ) found that teachers’ personal characteristics (e.g., anxiety, self-efficacy) are associated with their satisfaction and engagement with technology. Bingimlas ( 2009 ) reported that lack of confidence, resistance to change, and negative attitudes in using new technologies in teaching are significant determinants of teachers’ levels of engagement in ICT. The same author reported that the provision of technical support, motivation support (e.g., awards, sufficient time for planning), and training on how technologies can benefit teaching and learning can eliminate the above barriers to ICT integration. Archer et al. ( 2014 ) found that comfort levels in using technology are an important predictor of technology integration and argued that it is essential to provide teachers with appropriate training and ongoing support until they are comfortable with using ICTs in the classroom. Hillmayr et al. ( 2020 ) documented that training teachers on ICT had an important effecton students’ learning.

According to Balanskat et al. ( 2006 ), the impact of ICTs on students’ learning is highly dependent on the teachers’ capacity to efficiently exploit their application for pedagogical purposes. Results obtained from the Teaching and Learning International Survey (TALIS) (OECD, 2021 ) revealed that although schools are open to innovative practices and have the capacity to adopt them, only 39% of teachers in the European Union reported that they are well or very well prepared to use digital technologies for teaching. Li and Ma ( 2010 ) and Hardman ( 2019 ) showed that the positive effect of technology on students’ achievement depends on the pedagogical practices used by teachers. Schmid et al. ( 2014 ) reported that learning was best supported when students were engaged in active, meaningful activities with the use of technological tools that provided cognitive support. Tamim et al. ( 2015 ) compared two different pedagogical uses of tablets and found a significant moderate effect when the devices were used in a student-centered context and approach rather than within teacher-led environments. Similarly, Garzón and Acevedo ( 2019 ) and Garzón et al. ( 2020 ) reported that the positive results from the integration of AR applications could be attributed to the existence of different variables which could influence AR interventions (e.g., pedagogical approach, learning environment, and duration of the intervention). Additionally, Garzón et al. ( 2020 ) suggested that the pedagogical resources that teachers used to complement their lectures and the pedagogical approaches they applied were crucial to the effective integration of AR on students’ learning gains. Garzón and Acevedo ( 2019 ) also emphasized that the success of a technology-enhanced intervention is based on both the technology per se and its characteristics and on the pedagogical strategies teachers choose to implement. For instance, their results indicated that the collaborative learning approach had the highest impact on students’ learning gains among other approaches (e.g., inquiry-based learning, situated learning, or project-based learning). Ran et al. ( 2022 ) also found that the use of technology to design collaborative and communicative environments showed the largest moderator effects among the other approaches.

Hattie ( 2008 ) reported that the effective use of computers is associated with training teachers in using computers as a teaching and learning tool. Zheng et al. ( 2016 ) noted that in addition to the strategies teachers adopt in teaching, ongoing professional development is also vital in ensuring the success of technology implementation programs. Sung et al. ( 2016 ) found that research on the use of mobile devices to support learning tends to report that the insufficient preparation of teachers is a major obstacle in implementing effective mobile learning programs in schools. Friedel et al. ( 2013 ) found that providing training and support to teachers increased the positive impact of the interventions on students’ learning gains. Trucano ( 2005 ) argued that positive impacts occur when digital technologies are used to enhance teachers’ existing pedagogical philosophies. Higgins et al. ( 2012 ) found that the types of technologies used and how they are used could also affect students’ learning. The authors suggested that training and professional development of teachers that focuses on the effective pedagogical use of technology to support teaching and learning is an important component of successful instructional approaches (Higgins et al., 2012 ). Archer et al. ( 2014 ) found that studies that reported ICT interventions during which teachers received training and support had moderate positive effects on students’ learning outcomes, which were significantly higher than studies where little or no detail about training and support was mentioned. Fu ( 2013 ) reported that the lack of teachers’ knowledge and skills on the technical and instructional aspects of ICT use in the classroom, in-service training, pedagogy support, technical and financial support, as well as the lack of teachers’ motivation and encouragement to integrate ICT on their teaching were significant barriers to the integration of ICT in education.

School leadership and management

Management and leadership are important cornerstones in the digital transformation process (Pihir et al., 2018 ). Zheng et al. ( 2016 ) documented leadership among the factors positively affecting the successful implementation of technology integration in schools. Strong leadership, strategic planning, and systematic integration of digital technologies are prerequisites for the digital transformation of education systems (Ređep, 2021 ). Management and leadership play a significant role in formulating policies that are translated into practice and ensure that developments in ICT become embedded into the life of the school and in the experiences of staff and pupils (Condie & Munro, 2007 ). Policy support and leadership must include the provision of an overall vision for the use of digital technologies in education, guidance for students and parents, logistical support, as well as teacher training (Conrads et al., 2017 ). Unless there is a commitment throughout the school, with accountability for progress at key points, it is unlikely for ICT integration to be sustained or become part of the culture (Condie & Munro, 2007 ). To achieve this, principals need to adopt and promote a whole-institution strategy and build a strong mutual support system that enables the school’s technological maturity (European Commission, 2019 ). In this context, school culture plays an essential role in shaping the mindsets and beliefs of school actors towards successful technology integration. Condie and Munro ( 2007 ) emphasized the importance of the principal’s enthusiasm and work as a source of inspiration for the school staff and the students to cultivate a culture of innovation and establish sustainable digital change. Specifically, school leaders need to create conditions in which the school staff is empowered to experiment and take risks with technology (Elkordy & Lovinelli, 2020 ).

In order for leaders to achieve the above, it is important to develop capacities for learning and leading, advocating professional learning, and creating support systems and structures (European Commission, 2019 ). Digital technology integration in education systems can be challenging and leadership needs guidance to achieve it. Such guidance can be introduced through the adoption of new methods and techniques in strategic planning for the integration of digital technologies (Ređep, 2021 ). Even though the role of leaders is vital, the relevant training offered to them has so far been inadequate. Specifically, only a third of the education systems in Europe have put in place national strategies that explicitly refer to the training of school principals (European Commission, 2019 , p. 16).

Connectivity, infrastructure, and government and other support

The effective integration of digital technologies across levels of education presupposes the development of infrastructure, the provision of digital content, and the selection of proper resources (Voogt et al., 2013 ). Particularly, a high-quality broadband connection in the school increases the quality and quantity of educational activities. There is evidence that ICT increases and formalizes cooperative planning between teachers and cooperation with managers, which in turn has a positive impact on teaching practices (Balanskat et al., 2006 ). Additionally, ICT resources, including software and hardware, increase the likelihood of teachers integrating technology into the curriculum to enhance their teaching practices (Delgado et al., 2015 ). For example, Zheng et al. ( 2016 ) found that the use of one-on-one laptop programs resulted in positive changes in teaching and learning, which would not have been accomplished without the infrastructure and technical support provided to teachers. Delgado et al. ( 2015 ) reported that limited access to technology (insufficient computers, peripherals, and software) and lack of technical support are important barriers to ICT integration. Access to infrastructure refers not only to the availability of technology in a school but also to the provision of a proper amount and the right types of technology in locations where teachers and students can use them. Effective technical support is a central element of the whole-school strategy for ICT (Underwood, 2009 ). Bingimlas ( 2009 ) reported that lack of technical support in the classroom and whole-school resources (e.g., failing to connect to the Internet, printers not printing, malfunctioning computers, and working on old computers) are significant barriers that discourage the use of ICT by teachers. Moreover, poor quality and inadequate hardware maintenance, and unsuitable educational software may discourage teachers from using ICTs (Balanskat et al., 2006 ; Bingimlas, 2009 ).

Government support can also impact the integration of ICTs in teaching. Specifically, Balanskat et al. ( 2006 ) reported that government interventions and training programs increased teachers’ enthusiasm and positive attitudes towards ICT and led to the routine use of embedded ICT.

Lastly, another important factor affecting digital transformation is the development and quality assurance of digital learning resources. Such resources can be support textbooks and related materials or resources that focus on specific subjects or parts of the curriculum. Policies on the provision of digital learning resources are essential for schools and can be achieved through various actions. For example, some countries are financing web portals that become repositories, enabling teachers to share resources or create their own. Additionally, they may offer e-learning opportunities or other services linked to digital education. In other cases, specific agencies of projects have also been set up to develop digital resources (Eurydice, 2019 ).

Administration and digital data management

The digital transformation of schools involves organizational improvements at the level of internal workflows, communication between the different stakeholders, and potential for collaboration. Vuorikari et al. ( 2020 ) presented evidence that digital technologies supported the automation of administrative practices in schools and reduced the administration’s workload. There is evidence that digital data affects the production of knowledge about schools and has the power to transform how schooling takes place. Specifically, Sellar ( 2015 ) reported that data infrastructure in education is developing due to the demand for “ information about student outcomes, teacher quality, school performance, and adult skills, associated with policy efforts to increase human capital and productivity practices ” (p. 771). In this regard, practices, such as datafication which refers to the “ translation of information about all kinds of things and processes into quantified formats” have become essential for decision-making based on accountability reports about the school’s quality. The data could be turned into deep insights about education or training incorporating ICTs. For example, measuring students’ online engagement with the learning material and drawing meaningful conclusions can allow teachers to improve their educational interventions (Vuorikari et al., 2020 ).

Students’ socioeconomic background and family support

Research show that the active engagement of parents in the school and their support for the school’s work can make a difference to their children’s attitudes towards learning and, as a result, their achievement (Hattie, 2008 ). In recent years, digital technologies have been used for more effective communication between school and family (Escueta et al., 2017 ). The European Commission ( 2020 ) presented data from a Eurostat survey regarding the use of computers by students during the pandemic. The data showed that younger pupils needed additional support and guidance from parents and the challenges were greater for families in which parents had lower levels of education and little to no digital skills.

In this regard, the socio-economic background of the learners and their socio-cultural environment also affect educational achievements (Punie et al., 2006 ). Trucano documented that the use of computers at home positively influenced students’ confidence and resulted in more frequent use at school, compared to students who had no home access (Trucano, 2005 ). In this sense, the socio-economic background affects the access to computers at home (OECD, 2015 ) which in turn influences the experience of ICT, an important factor for school achievement (Punie et al., 2006 ; Underwood, 2009 ). Furthermore, parents from different socio-economic backgrounds may have different abilities and availability to support their children in their learning process (Di Pietro et al., 2020 ).

Schools’ socioeconomic context and emergency situations

The socio-economic context of the school is closely related to a school’s digital transformation. For example, schools in disadvantaged, rural, or deprived areas are likely to lack the digital capacity and infrastructure required to adapt to the use of digital technologies during emergency periods, such as the COVID-19 pandemic (Di Pietro et al., 2020 ). Data collected from school principals confirmed that in several countries, there is a rural/urban divide in connectivity (OECD, 2015 ).

Emergency periods also affect the digitalization of schools. The COVID-19 pandemic led to the closure of schools and forced them to seek appropriate and connective ways to keep working on the curriculum (Di Pietro et al., 2020 ). The sudden large-scale shift to distance and online teaching and learning also presented challenges around quality and equity in education, such as the risk of increased inequalities in learning, digital, and social, as well as teachers facing difficulties coping with this demanding situation (European Commission, 2020 ).

Looking at the findings of the above studies, we can conclude that the impact of digital technologies on education is influenced by various actors and touches many aspects of the school ecosystem. Figure  1 summarizes the factors affecting the digital technologies’ impact on school stakeholders based on the findings from the literature review.

Factors that affect the impact of ICTs on education

The findings revealed that the use of digital technologies in education affects a variety of actors within a school’s ecosystem. First, we observed that as technologies evolve, so does the interest of the research community to apply them to school settings. Figure  2 summarizes the trends identified in current research around the impact of digital technologies on schools’ digital capacity and transformation as found in the present study. Starting as early as 2005, when computers, simulations, and interactive boards were the most commonly applied tools in school interventions (e.g., Eng, 2005 ; Liao et al., 2007 ; Moran et al., 2008 ; Tamim et al., 2011 ), moving towards the use of learning platforms (Jewitt et al., 2011 ), then to the use of mobile devices and digital games (e.g., Tamim et al., 2015 ; Sung et al., 2016 ; Talan et al., 2020 ), as well as e-books (e.g., Savva et al., 2022 ), to the more recent advanced technologies, such as AR and VR applications (e.g., Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ), or robotics and AI (e.g., Su & Yang, 2022 ; Su et al., 2022 ). As this evolution shows, digital technologies are a concept in flux with different affordances and characteristics. Additionally, from an instructional perspective, there has been a growing interest in different modes and models of content delivery such as online, blended, and hybrid modes (e.g., Cheok & Wong, 2015 ; Kazu & Yalçin, 2022 ; Ulum, 2022 ). This is an indication that the value of technologies to support teaching and learning as well as other school-related practices is increasingly recognized by the research and school community. The impact results from the literature review indicate that ICT integration on students’ learning outcomes has effects that are small (Coban et al., 2022 ; Eng, 2005 ; Higgins et al., 2012 ; Schmid et al., 2014 ; Tamim et al., 2015 ; Zheng et al., 2016 ) to moderate (Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Liao et al., 2007 ; Sung et al., 2016 ; Talan et al., 2020 ; Wen & Walters, 2022 ). That said, a number of recent studies have reported high effect sizes (e.g., Kazu & Yalçin, 2022 ).

Current work and trends in the study of the impact of digital technologies on schools’ digital capacity

Based on these findings, several authors have suggested that the impact of technology on education depends on several variables and not on the technology per se (Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Lei et al., 2022a ). While the impact of ICTs on student achievement has been thoroughly investigated by researchers, other aspects related to school life that are also affected by ICTs, such as equality, inclusion, and social integration have received less attention. Further analysis of the literature review has revealed a greater investment in ICT interventions to support learning and teaching in the core subjects of literacy and STEM disciplines, especially mathematics, and science. These were the most common subjects studied in the reviewed papers often drawing on national testing results, while studies that investigated other subject areas, such as social studies, were limited (Chauhan, 2017 ; Condie & Munro, 2007 ). As such, research is still lacking impact studies that focus on the effects of ICTs on a range of curriculum subjects.

The qualitative research provided additional information about the impact of digital technologies on education, documenting positive effects and giving more details about implications, recommendations, and future research directions. Specifically, the findings regarding the role of ICTs in supporting learning highlight the importance of teachers’ instructional practice and the learning context in the use of technologies and consequently their impact on instruction (Çelik, 2022 ; Schmid et al., 2014 ; Tamim et al., 2015 ). The review also provided useful insights regarding the various factors that affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the transformation process. Specifically, these factors include a) digital competencies; b) teachers’ personal characteristics and professional development; c) school leadership and management; d) connectivity, infrastructure, and government support; e) administration and data management practices; f) students’ socio-economic background and family support and g) the socioeconomic context of the school and emergency situations. It is worth noting that we observed factors that affect the integration of ICTs in education but may also be affected by it. For example, the frequent use of ICTs and the use of laptops by students for instructional purposes positively affect the development of digital competencies (Zheng et al., 2016 ) and at the same time, the digital competencies affect the use of ICTs (Fu, 2013 ; Higgins et al., 2012 ). As a result, the impact of digital technologies should be explored more as an enabler of desirable and new practices and not merely as a catalyst that improves the output of the education process i.e. namely student attainment.

Conclusions

Digital technologies offer immense potential for fundamental improvement in schools. However, investment in ICT infrastructure and professional development to improve school education are yet to provide fruitful results. Digital transformation is a complex process that requires large-scale transformative changes that presuppose digital capacity and preparedness. To achieve such changes, all actors within the school’s ecosystem need to share a common vision regarding the integration of ICTs in education and work towards achieving this goal. Our literature review, which synthesized quantitative and qualitative data from a list of meta-analyses and review studies, provided useful insights into the impact of ICTs on different school stakeholders and showed that the impact of digital technologies touches upon many different aspects of school life, which are often overlooked when the focus is on student achievement as the final output of education. Furthermore, the concept of digital technologies is a concept in flux as technologies are not only different among them calling for different uses in the educational practice but they also change through time. Additionally, we opened a forum for discussion regarding the factors that affect a school’s digital capacity and transformation. We hope that our study will inform policy, practice, and research and result in a paradigm shift towards more holistic approaches in impact and assessment studies.

Study limitations and future directions

We presented a review of the study of digital technologies' impact on education and factors influencing schools’ digital capacity and transformation. The study results were based on a non-systematic literature review grounded on the acquisition of documentation in specific databases. Future studies should investigate more databases to corroborate and enhance our results. Moreover, search queries could be enhanced with key terms that could provide additional insights about the integration of ICTs in education, such as “policies and strategies for ICT integration in education”. Also, the study drew information from meta-analyses and literature reviews to acquire evidence about the effects of ICT integration in schools. Such evidence was mostly based on the general conclusions of the studies. It is worth mentioning that, we located individual studies which showed different, such as negative or neutral results. Thus, further insights are needed about the impact of ICTs on education and the factors influencing the impact. Furthermore, the nature of the studies included in meta-analyses and reviews is different as they are based on different research methodologies and data gathering processes. For instance, in a meta-analysis, the impact among the studies investigated is measured in a particular way, depending on policy or research targets (e.g., results from national examinations, pre-/post-tests). Meanwhile, in literature reviews, qualitative studies offer additional insights and detail based on self-reports and research opinions on several different aspects and stakeholders who could affect and be affected by ICT integration. As a result, it was challenging to draw causal relationships between so many interrelating variables.

Despite the challenges mentioned above, this study envisaged examining school units as ecosystems that consist of several actors by bringing together several variables from different research epistemologies to provide an understanding of the integration of ICTs. However, the use of other tools and methodologies and models for evaluation of the impact of digital technologies on education could give more detailed data and more accurate results. For instance, self-reflection tools, like SELFIE—developed on the DigCompOrg framework- (Kampylis et al., 2015 ; Bocconi & Lightfoot, 2021 ) can help capture a school’s digital capacity and better assess the impact of ICTs on education. Furthermore, the development of a theory of change could be a good approach for documenting the impact of digital technologies on education. Specifically, theories of change are models used for the evaluation of interventions and their impact; they are developed to describe how interventions will work and give the desired outcomes (Mayne, 2015 ). Theory of change as a methodological approach has also been used by researchers to develop models for evaluation in the field of education (e.g., Aromatario et al., 2019 ; Chapman & Sammons, 2013 ; De Silva et al., 2014 ).

We also propose that future studies aim at similar investigations by applying more holistic approaches for impact assessment that can provide in-depth data about the impact of digital technologies on education. For instance, future studies could focus on different research questions about the technologies that are used during the interventions or the way the implementation takes place (e.g., What methodologies are used for documenting impact? How are experimental studies implemented? How can teachers be taken into account and trained on the technology and its functions? What are the elements of an appropriate and successful implementation? How is the whole intervention designed? On which learning theories is the technology implementation based?).

Future research could also focus on assessing the impact of digital technologies on various other subjects since there is a scarcity of research related to particular subjects, such as geography, history, arts, music, and design and technology. More research should also be done about the impact of ICTs on skills, emotions, and attitudes, and on equality, inclusion, social interaction, and special needs education. There is also a need for more research about the impact of ICTs on administration, management, digitalization, and home-school relationships. Additionally, although new forms of teaching and learning with the use of ICTs (e.g., blended, hybrid, and online learning) have initiated several investigations in mainstream classrooms, only a few studies have measured their impact on students’ learning. Additionally, our review did not document any study about the impact of flipped classrooms on K-12 education. Regarding teaching and learning approaches, it is worth noting that studies referred to STEM or STEAM did not investigate the impact of STEM/STEAM as an interdisciplinary approach to learning but only investigated the impact of ICTs on learning in each domain as a separate subject (science, technology, engineering, arts, mathematics). Hence, we propose future research to also investigate the impact of the STEM/STEAM approach on education. The impact of emerging technologies on education, such as AR, VR, robotics, and AI has also been investigated recently, but more work needs to be done.

Finally, we propose that future studies could focus on the way in which specific factors, e.g., infrastructure and government support, school leadership and management, students’ and teachers’ digital competencies, approaches teachers utilize in the teaching and learning (e.g., blended, online and hybrid learning, flipped classrooms, STEM/STEAM approach, project-based learning, inquiry-based learning), affect the impact of digital technologies on education. We hope that future studies will give detailed insights into the concept of schools’ digital transformation through further investigation of impacts and factors which influence digital capacity and transformation based on the results and the recommendations of the present study.

Acknowledgements

This project has received funding under Grant Agreement No Ref Ares (2021) 339036 7483039 as well as funding from the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy. The UVa co-authors would like also to acknowledge funding from the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science and Innovation, under project grant PID2020-112584RB-C32.

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• Archer K, Savage R, Sanghera-Sidhu S, Wood E, Gottardo A, Chen V. Examining the effectiveness of technology use in classrooms: A tertiary meta-analysis. Computers & Education. 2014; 78 :140–149. doi: 10.1016/j.compedu.2014.06.001. [ CrossRef ] [ Google Scholar ]
• Aromatario O, Van Hoye A, Vuillemin A, Foucaut AM, Pommier J, Cambon L. Using theory of change to develop an intervention theory for designing and evaluating behavior change SDApps for healthy eating and physical exercise: The OCAPREV theory. BMC Public Health. 2019; 19 (1):1–12. doi: 10.1186/s12889-019-7828-4. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Arztmann, M., Hornstra, L., Jeuring, J., & Kester, L. (2022). Effects of games in STEM education: A meta-analysis on the moderating role of student background characteristics. Studies in Science Education , 1-37. 10.1080/03057267.2022.2057732
• Bado N. Game-based learning pedagogy: A review of the literature. Interactive Learning Environments. 2022; 30 (5):936–948. doi: 10.1080/10494820.2019.1683587. [ CrossRef ] [ Google Scholar ]
• Balanskat, A. (2009). Study of the impact of technology in primary schools – Synthesis Report. Empirica and European Schoolnet. Retrieved 30 June 2022 from: https://erte.dge.mec.pt/sites/default/files/Recursos/Estudos/synthesis_report_steps_en.pdf
• Balanskat, A. (2006). The ICT Impact Report: A review of studies of ICT impact on schools in Europe, European Schoolnet. Retrieved 30 June 2022 from:  https://en.unesco.org/icted/content/ict-impact-report-review-studies-ict-impact-schools-europe
• Balanskat, A., Blamire, R., & Kefala, S. (2006). The ICT impact report.  European Schoolnet . Retrieved from: http://colccti.colfinder.org/sites/default/files/ict_impact_report_0.pdf
• Balyer, A., & Öz, Ö. (2018). Academicians’ views on digital transformation in education. International Online Journal of Education and Teaching (IOJET), 5 (4), 809–830. Retrieved 30 June 2022 from  http://iojet.org/index.php/IOJET/article/view/441/295
• Baragash RS, Al-Samarraie H, Moody L, Zaqout F. Augmented reality and functional skills acquisition among individuals with special needs: A meta-analysis of group design studies. Journal of Special Education Technology. 2022; 37 (1):74–81. doi: 10.1177/0162643420910413. [ CrossRef ] [ Google Scholar ]
• Bates, A. W. (2015). Teaching in a digital age: Guidelines for designing teaching and learning . Open Educational Resources Collection . 6. Retrieved 30 June 2022 from: https://irl.umsl.edu/oer/6
• Bingimlas KA. Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. Eurasia Journal of Mathematics, Science and Technology Education. 2009; 5 (3):235–245. doi: 10.12973/ejmste/75275. [ CrossRef ] [ Google Scholar ]
• Blaskó Z, Costa PD, Schnepf SV. Learning losses and educational inequalities in Europe: Mapping the potential consequences of the COVID-19 crisis. Journal of European Social Policy. 2022; 32 (4):361–375. doi: 10.1177/09589287221091687. [ CrossRef ] [ Google Scholar ]
• Bocconi S, Lightfoot M. Scaling up and integrating the selfie tool for schools' digital capacity in education and training systems: Methodology and lessons learnt. European Training Foundation. 2021 doi: 10.2816/907029,JRC123936. [ CrossRef ] [ Google Scholar ]
• Brooks, D. C., & McCormack, M. (2020). Driving Digital Transformation in Higher Education . Retrieved 30 June 2022 from: https://library.educause.edu/-/media/files/library/2020/6/dx2020.pdf?la=en&hash=28FB8C377B59AFB1855C225BBA8E3CFBB0A271DA
• Cachia, R., Chaudron, S., Di Gioia, R., Velicu, A., & Vuorikari, R. (2021). Emergency remote schooling during COVID-19, a closer look at European families. Retrieved 30 June 2022 from  https://publications.jrc.ec.europa.eu/repository/handle/JRC125787
• Çelik B. The effects of computer simulations on students’ science process skills: Literature review. Canadian Journal of Educational and Social Studies. 2022; 2 (1):16–28. doi: 10.53103/cjess.v2i1.17. [ CrossRef ] [ Google Scholar ]
• Chapman, C., & Sammons, P. (2013). School Self-Evaluation for School Improvement: What Works and Why? . CfBT Education Trust. 60 Queens Road, Reading, RG1 4BS, England.
• Chauhan S. A meta-analysis of the impact of technology on learning effectiveness of elementary students. Computers & Education. 2017; 105 :14–30. doi: 10.1016/j.compedu.2016.11.005. [ CrossRef ] [ Google Scholar ]
• Chen, Q., Chan, K. L., Guo, S., Chen, M., Lo, C. K. M., & Ip, P. (2022a). Effectiveness of digital health interventions in reducing bullying and cyberbullying: a meta-analysis. Trauma, Violence, & Abuse , 15248380221082090. 10.1177/15248380221082090 [ PubMed ]
• Chen B, Wang Y, Wang L. The effects of virtual reality-assisted language learning: A meta-analysis. Sustainability. 2022; 14 (6):3147. doi: 10.3390/su14063147. [ CrossRef ] [ Google Scholar ]
• Cheok ML, Wong SL. Predictors of e-learning satisfaction in teaching and learning for school teachers: A literature review. International Journal of Instruction. 2015; 8 (1):75–90. doi: 10.12973/iji.2015.816a. [ CrossRef ] [ Google Scholar ]
• Cheung, A. C., & Slavin, R. E. (2011). The Effectiveness of Education Technology for Enhancing Reading Achievement: A Meta-Analysis. Center for Research and reform in Education .
• Coban, M., Bolat, Y. I., & Goksu, I. (2022). The potential of immersive virtual reality to enhance learning: A meta-analysis. Educational Research Review , 100452. 10.1016/j.edurev.2022.100452
• Condie, R., & Munro, R. K. (2007). The impact of ICT in schools-a landscape review. Retrieved 30 June 2022 from: https://oei.org.ar/ibertic/evaluacion/sites/default/files/biblioteca/33_impact_ict_in_schools.pdf
• Conrads, J., Rasmussen, M., Winters, N., Geniet, A., Langer, L., (2017). Digital Education Policies in Europe and Beyond: Key Design Principles for More Effective Policies. Redecker, C., P. Kampylis, M. Bacigalupo, Y. Punie (ed.), EUR 29000 EN, Publications Office of the European Union, Luxembourg, 10.2760/462941
• Costa P, Castaño-Muñoz J, Kampylis P. Capturing schools’ digital capacity: Psychometric analyses of the SELFIE self-reflection tool. Computers & Education. 2021; 162 :104080. doi: 10.1016/j.compedu.2020.104080. [ CrossRef ] [ Google Scholar ]
• Cussó-Calabuig R, Farran XC, Bosch-Capblanch X. Effects of intensive use of computers in secondary school on gender differences in attitudes towards ICT: A systematic review. Education and Information Technologies. 2018; 23 (5):2111–2139. doi: 10.1007/s10639-018-9706-6. [ CrossRef ] [ Google Scholar ]
• Daniel SJ. Education and the COVID-19 pandemic. Prospects. 2020; 49 (1):91–96. doi: 10.1007/s11125-020-09464-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Delcker J, Ifenthaler D. Teachers’ perspective on school development at German vocational schools during the Covid-19 pandemic. Technology, Pedagogy and Education. 2021; 30 (1):125–139. doi: 10.1080/1475939X.2020.1857826. [ CrossRef ] [ Google Scholar ]
• Delgado, A., Wardlow, L., O’Malley, K., & McKnight, K. (2015). Educational technology: A review of the integration, resources, and effectiveness of technology in K-12 classrooms. Journal of Information Technology Education Research , 14, 397. Retrieved 30 June 2022 from  http://www.jite.org/documents/Vol14/JITEv14ResearchP397-416Delgado1829.pdf
• De Silva MJ, Breuer E, Lee L, Asher L, Chowdhary N, Lund C, Patel V. Theory of change: A theory-driven approach to enhance the Medical Research Council's framework for complex interventions. Trials. 2014; 15 (1):1–13. doi: 10.1186/1745-6215-15-267. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Di Pietro G, Biagi F, Costa P, Karpiński Z, Mazza J. The likely impact of COVID-19 on education: Reflections based on the existing literature and recent international datasets. Publications Office of the European Union; 2020. [ Google Scholar ]
• Elkordy A, Lovinelli J. Competencies, Culture, and Change: A Model for Digital Transformation in K12 Educational Contexts. In: Ifenthaler D, Hofhues S, Egloffstein M, Helbig C, editors. Digital Transformation of Learning Organizations. Springer; 2020. pp. 203–219. [ Google Scholar ]
• Eng TS. The impact of ICT on learning: A review of research. International Education Journal. 2005; 6 (5):635–650. [ Google Scholar ]
• European Commission. (2020). Digital Education Action Plan 2021 – 2027. Resetting education and training for the digital age. Retrieved 30 June 2022 from  https://ec.europa.eu/education/sites/default/files/document-library-docs/deap-communication-sept2020_en.pdf
• European Commission. (2019). 2 nd survey of schools: ICT in education. Objective 1: Benchmark progress in ICT in schools . Retrieved 30 June 2022 from: https://data.europa.eu/euodp/data/storage/f/2019-03-19T084831/FinalreportObjective1-BenchmarkprogressinICTinschools.pdf
• Eurydice. (2019). Digital Education at School in Europe , Luxembourg: Publications Office of the European Union. Retrieved 30 June 2022 from: https://eacea.ec.europa.eu/national-policies/eurydice/content/digital-education-school-europe_en
• Escueta, M., Quan, V., Nickow, A. J., & Oreopoulos, P. (2017). Education technology: An evidence-based review. Retrieved 30 June 2022 from  https://ssrn.com/abstract=3031695
• Fadda D, Pellegrini M, Vivanet G, Zandonella Callegher C. Effects of digital games on student motivation in mathematics: A meta-analysis in K-12. Journal of Computer Assisted Learning. 2022; 38 (1):304–325. doi: 10.1111/jcal.12618. [ CrossRef ] [ Google Scholar ]
• Fernández-Gutiérrez M, Gimenez G, Calero J. Is the use of ICT in education leading to higher student outcomes? Analysis from the Spanish Autonomous Communities. Computers & Education. 2020; 157 :103969. doi: 10.1016/j.compedu.2020.103969. [ CrossRef ] [ Google Scholar ]
• Ferrari, A., Cachia, R., & Punie, Y. (2011). Educational change through technology: A challenge for obligatory schooling in Europe. Lecture Notes in Computer Science , 6964 , 97–110. Retrieved 30 June 2022  https://link.springer.com/content/pdf/10.1007/978-3-642-23985-4.pdf
• Fielding, K., & Murcia, K. (2022). Research linking digital technologies to young children’s creativity: An interpretive framework and systematic review. Issues in Educational Research , 32 (1), 105–125. Retrieved 30 June 2022 from  http://www.iier.org.au/iier32/fielding-abs.html
• Friedel, H., Bos, B., Lee, K., & Smith, S. (2013). The impact of mobile handheld digital devices on student learning: A literature review with meta-analysis. In Society for Information Technology & Teacher Education International Conference (pp. 3708–3717). Association for the Advancement of Computing in Education (AACE).
• Fu JS. ICT in education: A critical literature review and its implications. International Journal of Education and Development Using Information and Communication Technology (IJEDICT) 2013; 9 (1):112–125. [ Google Scholar ]
• Gaol FL, Prasolova-Førland E. Special section editorial: The frontiers of augmented and mixed reality in all levels of education. Education and Information Technologies. 2022; 27 (1):611–623. doi: 10.1007/s10639-021-10746-2. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Garzón J, Acevedo J. Meta-analysis of the impact of Augmented Reality on students’ learning gains. Educational Research Review. 2019; 27 :244–260. doi: 10.1016/j.edurev.2019.04.001. [ CrossRef ] [ Google Scholar ]
• Garzón, J., Baldiris, S., Gutiérrez, J., & Pavón, J. (2020). How do pedagogical approaches affect the impact of augmented reality on education? A meta-analysis and research synthesis. Educational Research Review , 100334. 10.1016/j.edurev.2020.100334
• Grgurović M, Chapelle CA, Shelley MC. A meta-analysis of effectiveness studies on computer technology-supported language learning. ReCALL. 2013; 25 (2):165–198. doi: 10.1017/S0958344013000013. [ CrossRef ] [ Google Scholar ]
• Haßler B, Major L, Hennessy S. Tablet use in schools: A critical review of the evidence for learning outcomes. Journal of Computer Assisted Learning. 2016; 32 (2):139–156. doi: 10.1111/jcal.12123. [ CrossRef ] [ Google Scholar ]
• Haleem A, Javaid M, Qadri MA, Suman R. Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers. 2022; 3 :275–285. doi: 10.1016/j.susoc.2022.05.004. [ CrossRef ] [ Google Scholar ]
• Hardman J. Towards a pedagogical model of teaching with ICTs for mathematics attainment in primary school: A review of studies 2008–2018. Heliyon. 2019; 5 (5):e01726. doi: 10.1016/j.heliyon.2019.e01726. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hattie J, Rogers HJ, Swaminathan H. The role of meta-analysis in educational research. In: Reid AD, Hart P, Peters MA, editors. A companion to research in education. Springer; 2014. pp. 197–207. [ Google Scholar ]
• Hattie J. Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge. 2008 doi: 10.4324/9780203887332. [ CrossRef ] [ Google Scholar ]
• Higgins S, Xiao Z, Katsipataki M. The impact of digital technology on learning: A summary for the education endowment foundation. Education Endowment Foundation and Durham University; 2012. [ Google Scholar ]
• Higgins, K., Huscroft-D’Angelo, J., & Crawford, L. (2019). Effects of technology in mathematics on achievement, motivation, and attitude: A meta-analysis. Journal of Educational Computing Research , 57(2), 283-319.
• Hillmayr D, Ziernwald L, Reinhold F, Hofer SI, Reiss KM. The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis. Computers & Education. 2020; 153 (1038):97. doi: 10.1016/j.compedu.2020.103897. [ CrossRef ] [ Google Scholar ]
• Istenic Starcic A, Bagon S. ICT-supported learning for inclusion of people with special needs: Review of seven educational technology journals, 1970–2011. British Journal of Educational Technology. 2014; 45 (2):202–230. doi: 10.1111/bjet.12086. [ CrossRef ] [ Google Scholar ]
• Jewitt C, Clark W, Hadjithoma-Garstka C. The use of learning platforms to organise learning in English primary and secondary schools. Learning, Media and Technology. 2011; 36 (4):335–348. doi: 10.1080/17439884.2011.621955. [ CrossRef ] [ Google Scholar ]
• JISC. (2020). What is digital transformation?.  Retrieved 30 June 2022 from: https://www.jisc.ac.uk/guides/digital-strategy-framework-for-university-leaders/what-is-digital-transformation
• Kalati, A. T., & Kim, M. S. (2022). What is the effect of touchscreen technology on young children’s learning?: A systematic review. Education and Information Technologies , 1-19. 10.1007/s10639-021-10816-5
• Kalemkuş, J., & Kalemkuş, F. (2022). Effect of the use of augmented reality applications on academic achievement of student in science education: Meta-analysis review. Interactive Learning Environments , 1-18. 10.1080/10494820.2022.2027458
• Kao C-W. The effects of digital game-based learning task in English as a foreign language contexts: A meta-analysis. Education Journal. 2014; 42 (2):113–141. [ Google Scholar ]
• Kampylis P, Punie Y, Devine J. Promoting effective digital-age learning - a European framework for digitally competent educational organisations. JRC Technical Reports. 2015 doi: 10.2791/54070. [ CrossRef ] [ Google Scholar ]
• Kazu IY, Yalçin CK. Investigation of the effectiveness of hybrid learning on academic achievement: A meta-analysis study. International Journal of Progressive Education. 2022; 18 (1):249–265. doi: 10.29329/ijpe.2022.426.14. [ CrossRef ] [ Google Scholar ]
• Koh C. A qualitative meta-analysis on the use of serious games to support learners with intellectual and developmental disabilities: What we know, what we need to know and what we can do. International Journal of Disability, Development and Education. 2022; 69 (3):919–950. doi: 10.1080/1034912X.2020.1746245. [ CrossRef ] [ Google Scholar ]
• König J, Jäger-Biela DJ, Glutsch N. Adapting to online teaching during COVID-19 school closure: Teacher education and teacher competence effects among early career teachers in Germany. European Journal of Teacher Education. 2020; 43 (4):608–622. doi: 10.1080/02619768.2020.1809650. [ CrossRef ] [ Google Scholar ]
• Lawrence JE, Tar UA. Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educational Media International. 2018; 55 (1):79–105. doi: 10.1080/09523987.2018.1439712. [ CrossRef ] [ Google Scholar ]
• Lee, S., Kuo, L. J., Xu, Z., & Hu, X. (2020). The effects of technology-integrated classroom instruction on K-12 English language learners’ literacy development: A meta-analysis. Computer Assisted Language Learning , 1-32. 10.1080/09588221.2020.1774612
• Lei, H., Chiu, M. M., Wang, D., Wang, C., & Xie, T. (2022a). Effects of game-based learning on students’ achievement in science: a meta-analysis. Journal of Educational Computing Research . 10.1177/07356331211064543
• Lei H, Wang C, Chiu MM, Chen S. Do educational games affect students' achievement emotions? Evidence from a meta-analysis. Journal of Computer Assisted Learning. 2022; 38 (4):946–959. doi: 10.1111/jcal.12664. [ CrossRef ] [ Google Scholar ]
• Liao YKC, Chang HW, Chen YW. Effects of computer application on elementary school student's achievement: A meta-analysis of students in Taiwan. Computers in the Schools. 2007; 24 (3–4):43–64. doi: 10.1300/J025v24n03_04. [ CrossRef ] [ Google Scholar ]
• Li Q, Ma X. A meta-analysis of the effects of computer technology on school students’ mathematics learning. Educational Psychology Review. 2010; 22 (3):215–243. doi: 10.1007/s10648-010-9125-8. [ CrossRef ] [ Google Scholar ]
• Liu, M., Pang, W., Guo, J., & Zhang, Y. (2022). A meta-analysis of the effect of multimedia technology on creative performance. Education and Information Technologies , 1-28. 10.1007/s10639-022-10981-1
• Lu Z, Chiu MM, Cui Y, Mao W, Lei H. Effects of game-based learning on students’ computational thinking: A meta-analysis. Journal of Educational Computing Research. 2022 doi: 10.1177/07356331221100740. [ CrossRef ] [ Google Scholar ]
• Martinez L, Gimenes M, Lambert E. Entertainment video games for academic learning: A systematic review. Journal of Educational Computing Research. 2022 doi: 10.1177/07356331211053848. [ CrossRef ] [ Google Scholar ]
• Mayne J. Useful theory of change models. Canadian Journal of Program Evaluation. 2015; 30 (2):119–142. doi: 10.3138/cjpe.230. [ CrossRef ] [ Google Scholar ]
• Moran J, Ferdig RE, Pearson PD, Wardrop J, Blomeyer RL., Jr Technology and reading performance in the middle-school grades: A meta-analysis with recommendations for policy and practice. Journal of Literacy Research. 2008; 40 (1):6–58. doi: 10.1080/10862960802070483. [ CrossRef ] [ Google Scholar ]
• OECD. (2015). Students, Computers and Learning: Making the Connection . PISA, OECD Publishing, Paris. Retrieved from: 10.1787/9789264239555-en
• OECD. (2021). OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. Retrieved from: https://www.oecd-ilibrary.org/education/oecd-digital-education-outlook-2021_589b283f-en
• Pan Y, Ke F, Xu X. A systematic review of the role of learning games in fostering mathematics education in K-12 settings. Educational Research Review. 2022; 36 :100448. doi: 10.1016/j.edurev.2022.100448. [ CrossRef ] [ Google Scholar ]
• Pettersson F. Understanding digitalization and educational change in school by means of activity theory and the levels of learning concept. Education and Information Technologies. 2021; 26 (1):187–204. doi: 10.1007/s10639-020-10239-8. [ CrossRef ] [ Google Scholar ]
• Pihir, I., Tomičić-Pupek, K., & Furjan, M. T. (2018). Digital transformation insights and trends. In Central European Conference on Information and Intelligent Systems (pp. 141–149). Faculty of Organization and Informatics Varazdin. Retrieved 30 June 2022 from https://www.proquest.com/conference-papers-proceedings/digital-transformation-insights-trends/docview/2125639934/se-2
• Punie, Y., Zinnbauer, D., & Cabrera, M. (2006). A review of the impact of ICT on learning. Working Paper prepared for DG EAC. Retrieved 30 June 2022 from: http://www.eurosfaire.prd.fr/7pc/doc/1224678677_jrc47246n.pdf
• Quah CY, Ng KH. A systematic literature review on digital storytelling authoring tool in education: January 2010 to January 2020. International Journal of Human-Computer Interaction. 2022; 38 (9):851–867. doi: 10.1080/10447318.2021.1972608. [ CrossRef ] [ Google Scholar ]
• Ran H, Kim NJ, Secada WG. A meta-analysis on the effects of technology's functions and roles on students' mathematics achievement in K-12 classrooms. Journal of computer assisted learning. 2022; 38 (1):258–284. doi: 10.1111/jcal.12611. [ CrossRef ] [ Google Scholar ]
• Ređep, N. B. (2021). Comparative overview of the digital preparedness of education systems in selected CEE countries. Center for Policy Studies. CEU Democracy Institute .
• Rott, B., & Marouane, C. (2018). Digitalization in schools–organization, collaboration and communication. In Digital Marketplaces Unleashed (pp. 113–124). Springer, Berlin, Heidelberg.
• Savva M, Higgins S, Beckmann N. Meta-analysis examining the effects of electronic storybooks on language and literacy outcomes for children in grades Pre-K to grade 2. Journal of Computer Assisted Learning. 2022; 38 (2):526–564. doi: 10.1111/jcal.12623. [ CrossRef ] [ Google Scholar ]
• Schmid RF, Bernard RM, Borokhovski E, Tamim RM, Abrami PC, Surkes MA, Wade CA, Woods J. The effects of technology use in postsecondary education: A meta-analysis of classroom applications. Computers & Education. 2014; 72 :271–291. doi: 10.1016/j.compedu.2013.11.002. [ CrossRef ] [ Google Scholar ]
• Schuele CM, Justice LM. The importance of effect sizes in the interpretation of research: Primer on research: Part 3. The ASHA Leader. 2006; 11 (10):14–27. doi: 10.1044/leader.FTR4.11102006.14. [ CrossRef ] [ Google Scholar ]
• Schwabe, A., Lind, F., Kosch, L., & Boomgaarden, H. G. (2022). No negative effects of reading on screen on comprehension of narrative texts compared to print: A meta-analysis. Media Psychology , 1-18. 10.1080/15213269.2022.2070216
• Sellar S. Data infrastructure: a review of expanding accountability systems and large-scale assessments in education. Discourse: Studies in the Cultural Politics of Education. 2015; 36 (5):765–777. doi: 10.1080/01596306.2014.931117. [ CrossRef ] [ Google Scholar ]
• Stock WA. Systematic coding for research synthesis. In: Cooper H, Hedges LV, editors. The handbook of research synthesis, 236. Russel Sage; 1994. pp. 125–138. [ Google Scholar ]
• Su, J., Zhong, Y., & Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence , 100065. 10.1016/j.caeai.2022.100065
• Su J, Yang W. Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence. 2022; 3 :100049. doi: 10.1016/j.caeai.2022.100049. [ CrossRef ] [ Google Scholar ]
• Sung YT, Chang KE, Liu TC. The effects of integrating mobile devices with teaching and learning on students' learning performance: A meta-analysis and research synthesis. Computers & Education. 2016; 94 :252–275. doi: 10.1016/j.compedu.2015.11.008. [ CrossRef ] [ Google Scholar ]
• Talan T, Doğan Y, Batdı V. Efficiency of digital and non-digital educational games: A comparative meta-analysis and a meta-thematic analysis. Journal of Research on Technology in Education. 2020; 52 (4):474–514. doi: 10.1080/15391523.2020.1743798. [ CrossRef ] [ Google Scholar ]
• Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011). What forty years of research says about the impact of technology on learning: A second-order meta-analysis and validation study. Review of Educational research, 81 (1), 4–28. Retrieved 30 June 2022 from 10.3102/0034654310393361
• Tamim, R. M., Borokhovski, E., Pickup, D., Bernard, R. M., & El Saadi, L. (2015). Tablets for teaching and learning: A systematic review and meta-analysis. Commonwealth of Learning. Retrieved from: http://oasis.col.org/bitstream/handle/11599/1012/2015_Tamim-et-al_Tablets-for-Teaching-and-Learning.pdf
• Tang C, Mao S, Xing Z, Naumann S. Improving student creativity through digital technology products: A literature review. Thinking Skills and Creativity. 2022; 44 :101032. doi: 10.1016/j.tsc.2022.101032. [ CrossRef ] [ Google Scholar ]
• Tolani-Brown, N., McCormac, M., & Zimmermann, R. (2011). An analysis of the research and impact of ICT in education in developing country contexts. In ICTs and sustainable solutions for the digital divide: Theory and perspectives (pp. 218–242). IGI Global.
• Trucano, M. (2005). Knowledge Maps: ICTs in Education. Washington, DC: info Dev / World Bank. Retrieved 30 June 2022 from  https://files.eric.ed.gov/fulltext/ED496513.pdf
• Ulum H. The effects of online education on academic success: A meta-analysis study. Education and Information Technologies. 2022; 27 (1):429–450. doi: 10.1007/s10639-021-10740-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Underwood, J. D. (2009). The impact of digital technology: A review of the evidence of the impact of digital technologies on formal education. Retrieved 30 June 2022 from: http://dera.ioe.ac.uk/id/eprint/10491
• Verschaffel, L., Depaepe, F., & Mevarech, Z. (2019). Learning Mathematics in metacognitively oriented ICT-Based learning environments: A systematic review of the literature. Education Research International , 2019 . 10.1155/2019/3402035
• Villena-Taranilla R, Tirado-Olivares S, Cózar-Gutiérrez R, González-Calero JA. Effects of virtual reality on learning outcomes in K-6 education: A meta-analysis. Educational Research Review. 2022; 35 :100434. doi: 10.1016/j.edurev.2022.100434. [ CrossRef ] [ Google Scholar ]
• Voogt J, Knezek G, Cox M, Knezek D, ten Brummelhuis A. Under which conditions does ICT have a positive effect on teaching and learning? A call to action. Journal of Computer Assisted Learning. 2013; 29 (1):4–14. doi: 10.1111/j.1365-2729.2011.00453.x. [ CrossRef ] [ Google Scholar ]
• Vuorikari, R., Punie, Y., & Cabrera, M. (2020). Emerging technologies and the teaching profession: Ethical and pedagogical considerations based on near-future scenarios  (No. JRC120183). Joint Research Centre. Retrieved 30 June 2022 from: https://publications.jrc.ec.europa.eu/repository/handle/JRC120183
• Wang LH, Chen B, Hwang GJ, Guan JQ, Wang YQ. Effects of digital game-based STEM education on students’ learning achievement: A meta-analysis. International Journal of STEM Education. 2022; 9 (1):1–13. doi: 10.1186/s40594-022-00344-0. [ CrossRef ] [ Google Scholar ]
• Wen X, Walters SM. The impact of technology on students’ writing performances in elementary classrooms: A meta-analysis. Computers and Education Open. 2022; 3 :100082. doi: 10.1016/j.caeo.2022.100082. [ CrossRef ] [ Google Scholar ]
• Zheng B, Warschauer M, Lin CH, Chang C. Learning in one-to-one laptop environments: A meta-analysis and research synthesis. Review of Educational Research. 2016; 86 (4):1052–1084. doi: 10.3102/0034654316628645. [ CrossRef ] [ Google Scholar ]

REVIEW article

Revolutionizing education: exploring the potential of ai-enabled brain-based learning for enhanced cognitive development provisionally accepted.

• 1 Illinois State University, United States

The final, formatted version of the article will be published soon.

This research paper is a systemic review of the literature on integration of AI technology and Brain-Based Learning Theories in education, aiming to address a significant research gap in exploring the potential of AI-Enabled Brain-Based Learning and highlight their potential impact on cognitive development. While both AI technology and Brain-Based Learning Theories have been extensively studied and applied independently in the field of education, there is a lack of comprehensive research exploring their combined potential. This study delves into the theoretical foundations and guiding principles of brain-based learning, investigating its application in personalizing and enhancing learning experiences. It evaluates the current state of AI technology in education and examines how AI-powered brain-based learning techniques can improve student engagement, knowledge retention, and critical thinking skills. Ethical considerations and challenges associated with integrating AI into brain-based learning methodologies are acknowledged, and practical guidelines are provided for educators and decision-makers to effectively leverage AI in implementation. The research also examines the long-term effects of AI-enabled brain-based learning on educational systems, workforce readiness, and lifelong learning opportunities. Drawing on case studies and best practices from successful academic institutions, valuable insights are presented regarding the synergistic relationship between brain-based learning and AI. The paper proposes strategies for scaling and implementing AI-based brain-based learning approaches across diverse educational settings, with the aim of driving future innovations and advancements in education. Ultimately, this research sheds light on the transformative potential of AI-enabled brain-based learning, opening new avenues for educational improvement and advancement.

Keywords: AI-enabled learning, brain-based learning, cognitive development, personalized learning, Educational Technology, student engagement

Received: 03 Aug 2023; Accepted: 05 Apr 2024.

Copyright: © 2024 Seaba. 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) or licensor 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: Mr. Victor E. Seaba, Illinois State University, Normal, United States

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