concept paper about lack of education

The Education Crisis: Being in School Is Not the Same as Learning

First grade students in Pakistan’s Balochistan Province are learning the alphabet through child-friendly flash cards. Their learning materials help educators teach through interactive and engaging activities and are provided free of charge through a student’s first learning backpack. © World Bank 

THE NAME OF THE DOG IS PUPPY. This seems like a simple sentence. But did you know that in Kenya, Tanzania, and Uganda, three out of four third grade students do not understand it? The world is facing a learning crisis . Worldwide, hundreds of millions of children reach young adulthood without even the most basic skills like calculating the correct change from a transaction, reading a doctor’s instructions, or understanding a bus schedule—let alone building a fulfilling career or educating their children. Education is at the center of building human capital. The latest World Bank research shows that the productivity of 56 percent of the world’s children will be less than half of what it could be if they enjoyed complete education and full health. For individuals, education raises self-esteem and furthers opportunities for employment and earnings. And for a country, it helps strengthen institutions within societies, drives long-term economic growth, reduces poverty, and spurs innovation.

A global learning crisis

As a result, it is hard for them to do anything about it. And with uncertainty about the kinds of skills the jobs of the future will require, schools and teachers must prepare students with more than basic reading and writing skills. Students need to be able to interpret information, form opinions, be creative, communicate well, collaborate, and be resilient. The World Bank’s vision is for all children and youth to be learning and acquiring the skills they need to be productive, fulfilled, and involved citizens and workers. Our focus is on helping teachers at all levels become more effective in facilitating learning, improving technology for learning, strengthening management of schools and systems, while ensuring learners of all ages—from preschool to adulthood—are equipped for success.

Change starts with a great teacher

A growing body of evidence suggests the learning crisis is, at its core, a teaching crisis. For students to learn, they need good teachers —but Fortunately for many students, in every country, there are dedicated and enthusiastic teachers who, despite all challenges, enrich and transform their lives. They are heroes who defy the odds and make learning happen with passion, creativity and determination.

One such hero works in the Ecoles Oued Eddahab school in Kenitra, Morocco. In a colorful classroom that she painted herself, she uses creative tools to make sure that every child learns, participates, and has fun. In her class, each letter in the alphabet is associated with the sound of an animal and a hand movement. During class she says a word, spells it out loud using the sounds and the movement, and students then write the word down. She can easily identify students who are struggling with the material and adjust the pace of the lesson to help them get on track. Children are engaged and attentive. They participate and are not afraid to make mistakes. This is a teacher who wants to make sure that ALL children learn. 

One of the most interesting, large scale educational technology efforts is being led by EkStep , a philanthropic effort in India. EkStep created an open digital infrastructure which provides access to learning opportunities for 200 million children, as well as professional development opportunities for 12 million teachers and 4.5 million school leaders. Both teachers and children are accessing content which ranges from teaching materials, explanatory videos, interactive content, stories, practice worksheets, and formative assessments. By monitoring which content is used most frequently—and most beneficially—informed decisions can be made around future content.

In the Dominican Republic, a World Bank supported pilot study shows how adaptive technologies can generate great interest among 21st century students and present a path to supporting the learning and teaching of future generations. Yudeisy, a sixth grader participating in the study, says that what she likes doing the most during the day is watching videos and tutorials on her computer and cell phone. Taking childhood curiosity as a starting point, the study aimed to channel it towards math learning in a way that interests Yudeisy and her classmates.

Yudeisy, along with her classmates in a public elementary school in Santo Domingo, is part of a four-month pilot to reinforce mathematics using software that adapts to the math level of each student. © World Bank

Adaptive technology was used to evaluate students’ initial learning level to then walk them through math exercises in a dynamic, personalized way, based on artificial intelligence and what the student is ready to learn. After three months, students with the lowest initial performance achieved substantial improvements. This shows the potential of technology to increase learning outcomes, especially among students lagging behind their peers. In a field that is developing at dizzying speeds, innovative solutions to educational challenges are springing up everywhere. Our challenge is to make technology a driver of equity and inclusion and not a source of greater inequality of opportunity. We are working with partners worldwide to support the effective and appropriate use of educational technologies to strengthen learning.

When schools and educations systems are managed well, learning happens

Successful education reforms require good policy design, strong political commitment, and effective implementation capacity . Of course, this is extremely challenging. Many countries struggle to make efficient use of resources and very often increased education spending does not translate into more learning and improved human capital. Overcoming such challenges involves working at all levels of the system.

At the central level, ministries of education need to attract the best experts to design and implement evidence-based and country-specific programs. District or regional offices need the capacity and the tools to monitor learning and support schools. At the school level, principals need to be trained and prepared to manage and lead schools, from planning the use of resources to supervising and nurturing their teachers. However difficult, change is possible. Supported by the World Bank, public schools across Punjab in Pakistan have been part of major reforms over the past few years to address these challenges. Through improved school-level accountability by monitoring and limiting teacher and student absenteeism, and the introduction of a merit-based teacher recruitment system, where only the most talented and motivated teachers were selected, they were able to increase enrollment and retention of students and significantly improve the quality of education. "The government schools have become very good now, even better than private ones," said Mr. Ahmed, a local villager.

The World Bank, along with the Bill and Melinda Gates Foundation, and the UK’s Department for International Development, is developing the Global Education Policy Dashboard . This new initiative will provide governments with a system for monitoring how their education systems are functioning, from learning data to policy plans, so they are better able to make timely and evidence-based decisions.

Education reform: The long game is worth it

In fact, it will take a generation to realize the full benefits of high-quality teachers, the effective use of technology, improved management of education systems, and engaged and prepared learners. However, global experience shows us that countries that have rapidly accelerated development and prosperity all share the common characteristic of taking education seriously and investing appropriately. As we mark the first-ever International Day of Education on January 24, we must do all we can to equip our youth with the skills to keep learning, adapt to changing realities, and thrive in an increasingly competitive global economy and a rapidly changing world of work.

The schools of the future are being built today. These are schools where all teachers have the right competencies and motivation, where technology empowers them to deliver quality learning, and where all students learn fundamental skills, including socio-emotional, and digital skills. These schools are safe and affordable to everyone and are places where children and young people learn with joy, rigor, and purpose. Governments, teachers, parents, and the international community must do their homework to realize the promise of education for all students, in every village, in every city, and in every country. 

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• Published: 25 December 2019

Mapping disparities in education across low- and middle-income countries

Local burden of disease educational attainment collaborators.

Nature volume  577 ,  pages 235–238 ( 2020 ) Cite this article

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Educational attainment is an important social determinant of maternal, newborn, and child health 1 , 2 , 3 . As a tool for promoting gender equity, it has gained increasing traction in popular media, international aid strategies, and global agenda-setting 4 , 5 , 6 . The global health agenda is increasingly focused on evidence of precision public health, which illustrates the subnational distribution of disease and illness 7 , 8 ; however, an agenda focused on future equity must integrate comparable evidence on the distribution of social determinants of health 9 , 10 , 11 . Here we expand on the available precision SDG evidence by estimating the subnational distribution of educational attainment, including the proportions of individuals who have completed key levels of schooling, across all low- and middle-income countries from 2000 to 2017. Previous analyses have focused on geographical disparities in average attainment across Africa or for specific countries, but—to our knowledge—no analysis has examined the subnational proportions of individuals who completed specific levels of education across all low- and middle-income countries 12 , 13 , 14 . By geolocating subnational data for more than 184 million person-years across 528 data sources, we precisely identify inequalities across geography as well as within populations.

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Role of birth order, gender, and region in educational attainment in Pakistan

Education, as a social determinant of health, is closely linked to several facets of the Sustainable Development Goals (SDGs) of the United Nations 2 . In addition to the explicit focus of SDG 4 on educational attainment, improved gender equality (SDG 5) and maternal, newborn, and child health (SDG 3) have well-documented associations with increased schooling 15 , 16 , 17 . In 2016, after years of deprioritization, aid to education reached its highest level since 2002 18 . Despite this shift, only 22% of aid to basic education—defined as primary and lower-secondary—went to low-income countries in 2016 compared to 36% in 2002 19 . This reflects a persistent pattern in which the distribution of aid does not align with the greatest need, even at the national level. Beyond international aid, domestic policy is also a crucial tool for expanding access to education, especially at higher levels. However, policy-makers often do not have access to a rigorous evidence base at a subnational level. This analysis presents the subnational distribution of education to support the growing evidence base of precision public health data, which shows widespread disparity of health outcomes as well as their social determinants.

Mapping education across gender

Despite widespread improvement in educational attainment since 2000, gender disparity persists in 2017 in many regions. Figure 1 illustrates the mean number of years of education and the proportion of individuals with no primary school attainment for men and women of reproductive age (15–49 years) in 2017. The average educational attainment is very low across much of the Sahel region of sub-Saharan Africa, consistent with previously published data 14 . In 2017, there was a large gender disparity in many regions, with men attaining higher average education across central and western sub-Saharan Africa and South Asia. Considerable variation remains between the highest- and lowest-performing administrative units within countries in 2017. For Uganda in 2017, this indicator ranged from 1.9 years of education (95% uncertainty interval, 0.8–3.0 years) in rural Kotido to 11.1 years (10.1–12 years) in Kampala, the capital city. Figure 1b, d displays the proportion of men and women aged 15–49 years who have not completed primary school. By considering the variation within populations in different locations, these maps help to identify areas with large populations in the vulnerable lower end of the attainment distribution. We estimated large improvements in the proportions of individuals who have completed primary school in Mexico and China. However, across much of the world women in this age group failed to complete primary school at a much higher rate than their male counterparts.

a–d , Mean educational attainment for women ( a ) and men ( c ) and the proportion of individuals with no primary school education for women ( b ) and men ( d ) aged 15–49 years in 2017. Maps were produced using ArcGIS Desktop 10.6.

Despite continued lack of gender parity in education among the reproductive age group, vast progress towards parity has been made among the 20–24 age group. Extended Data Fig. 2 further examines gender parity in 2000 and 2017. This figure highlights two additional advantages of our analytic framework. First, we examined a younger group aged 20–24 years. Although education in this group is less directly relevant to maternal, newborn, and child health than education in the full window of reproductive age, these estimates allowed us to capture how the landscape of education has shifted over time (that is, across successive cohorts) and is therefore more likely to pick up improvements to access and retention in education systems that have been made since 2000. Second, we illustrate the probability that this estimated ratio is credibly different from 1 (parity between sexes) given the full uncertainty in our data and model. In 2000, we estimated that men completed schooling at a higher rate than women across much of the world, particularly for primary school education (that is, the probability that the parity ratio is greater than 1 was over 95%). This was true in most countries for both primary and secondary completion rates, but especially so in Burundi, Angola, Uganda, and Afghanistan (Extended Data Fig. 2a, c ). By 2017, many countries moved significantly towards parity in both secondary and primary completion rates with the exception of large regions within central and western sub-Saharan Africa (Extended Data Fig. 2b, d ).

Inequalities within and between countries

The subnational estimates of attainment presented here enable a closer examination of within-country inequality and associated trends over time. Figure 2 plots the national change in secondary attainment rates for women aged 20–24 years with the index of dissimilarity across second administrative-level units in 2017. The index of dissimilarity is an intuitive measure of geographical inequality that can be interpreted as the percentage of women with secondary attainment that would have to move in order to equalize secondary rates across all subnational districts. We estimated that countries that experienced more national progress over the period tended to be more spatially equal in 2017. However, the top-right quadrant of the graph highlights several countries that experienced substantial national progress yet remain some of the most geographically unequal countries today.

a , Change in secondary attainment rates for women age 20–24 years between 2000 and 2017 compared with the national index of dissimilarity in 2017 (simple linear regression lines are included). b , Map of the national index of dissimilarity in 2017. Maps were produced using ArcGIS Desktop 10.6.

We further examined national progress between 2000 and 2017 in two such countries, India and Nigeria, where rates of secondary attainment increased from 10.9% (8.5–12.5%) to 37.2% (33.6–41.1%) and from 11.5% (6.2–18.3%) to 45.0% (37.0–52.5%), respectively (Fig. 3 ). The geographical distribution between two cohorts—women aged 20–24 years in 2000 and 2017—was analysed by examining all proportions simultaneously (Fig. 3a, b ). We estimate that there has been a massive shift towards primary and secondary completion coupled with greater geographical variability in completion rates (that is, spread of the dots that represent subnational units in the legend). The majority of the 2017 cohort living in the northwest and northeast of India never completed secondary school. Urban centres in the south, such as Bangalore and Mumbai, have seen considerable progress compared with more rural regions. In Nigeria, we estimate substantial national improvement; however, the country remained one of the most spatially unequal in 2017 (Fig. 3d, e ). The more-urban south, particularly around Lagos, experienced much faster progress than the more-rural north. The implications of the population distribution were explored by decomposing the improvement in the national rate of secondary completion since 2000 for each country into the additive contributions of rate changes at the second administrative level (Fig. 3c, f ). This demonstrates that national progress was largely driven by improvements in populous urban regions (particularly Maharashtra, India, and Lagos, Nigeria), underscoring the importance of how subnational progress (or lack thereof) contributes differentially to narratives surrounding national change.

a , b , Attainment rates for women aged 20–24 years in 2000 ( a ) and 2017 ( b ) at the second administrative level in India. c , Additive contributions of changes in the attainment rates at the second administrative level to change in the rate at the national level between 2000 and 2017 in India. d , e , Attainment rates for women aged 20–24 years in 2000 and 2017 at the second administrative level in Nigeria. f , Additive contributions of changes in the attainment rates at the second administrative level to change in the rate at the national level between 2000 and 2017 in Nigeria. On all ternary maps, the ‘Zero’ category includes all individuals with either no schooling or some primary schooling without completion. Maps were produced using ArcGIS Desktop 10.6.

Discussion and limitations

We have built on previous modelling efforts that focused on the geographical distribution of average education 14 by extending our estimation to the distribution of attainment, highlighting not only average attainment but also the proportions of individuals who completed key levels of schooling that are central to policy efforts. As we demonstrate, throughout much of the world women lag behind their male counterparts, and there is significant heterogeneity across subnational regions. Countries such as South Africa, Peru, and Colombia have seen tremendous improvement since 2000 in the proportion of the young adult population who have completed secondary school. As this trend continues, it will be important to focus not only on attainment but also on quality of education. However, many young women across the world still faced obstacles to attaining even a basic level of education in 2017 (Extended Data Fig. 3 ). This represents a missed opportunity for the global health community to focus on a well-studied determinant of maternal, newborn, and child health. Even with only marginal returns to health in the short term, studies suggest that, on average, communities will also see increased human capital, social mobility, and less engagement in child marriage or early childbearing 20 , 21 .

Children and adolescents do not complete formal schooling for many reasons. Many factors differentially affect girls, such as cost, late or no school enrolment, forced withdrawal of married adolescents, and the social influence of family members concerning the traditional roles of girls and women 4 , 20 , 22 , 23 . A critical step is acknowledging that commercialization in the area of education typically leads to higher inequity 24 . Treating public education as a societal good by increasing access, particularly in underserved rural communities, reduces inequality. Identifying areas that are stagnating or worsening, particularly in the realm of basic education for young women across the world, is an important first step to targeted, long-term reform efforts that will ultimately have widespread benefits for equity in health and development.

Many recent international calls to improve the social determinants of health have stated that measurement of inequity within countries is critical to understanding and tracking the problem, noting that geography is an increasingly important dimension of inequity 24 , 25 , 26 . Where people are born greatly determines their life chances, and continuing to consider development and human capital formation on a national level is insufficient 24 . The goal of this analysis is to identify local areas that may have experienced negligible improvements, but further rigorous research is required to contextualize these patterns within the unique mix of structural obstacles that each community faces. There are many indirect costs for attending school and each disadvantaged area that we identify in our analysis may experience them in different ways. These include the demand for children to work, the opportunity or monetary costs of attending school, distance to school, lack of compulsory education requirements, high fees for attendance, political instability, and many other forces. Overcoming these obstacles to improve educational attainment alone will not necessarily result in a more-educated and healthy population for each country as highly educated individuals may be more likely to emigrate, resulting in ‘brain drain’. This is especially true for countries that have been economically crippled over the past two decades and may lack the economic capacity to absorb a more highly educated labour force. Opening access to education will need to be coupled with economic reforms, both internationally and domestically, if countries are to fully experience dividends in human capital and health.

Over the next decade of the SDG agenda, it will be important to maintain the progress that has been made to reprioritise investment in education systems. There remains an alarming lack of distributional accountability in aid, especially to basic education, for which most funding is not going to the countries that need it most 19 . Connections between educational attainment and health offer promising opportunities for co-financing initiatives. For example, USAID recently invested US$90 million in HIV funding to the construction of secondary schools in sub-Saharan Africa. Global health leaders have noted the need to invest in precise data systems and eliminate data gaps to effectively target resources, develop equitable policy, and track accountability 7 . Our analysis provides a robust evidence base for such decision-making and advocacy. Decades of research on the effect of basic education on maternal, newborn, and child health positions this issue squarely in the purview of the global health agenda. It is crucial for the global health community to invest in long-term, sustainable improvement in the underlying distribution of human capital, as this is the only way to truly influence health equity across generations.

Using a Bayesian model-based geostatistical framework and synthesizing geolocated data from 528 household and census datasets, this analysis provides subnational estimates of mean numbers years of education and the proportion of the population who attained key levels of education for women of reproductive age (15–49 years), women aged 20–24 years, and equivalent male age bins between 2000 and 2017 in 105 countries across all low- and middle-income countries (LMICs). Countries were selected for inclusion in this analysis using the socio-demographic index (SDI) published in the Global Burden of Disease (GBD) study 27 . The SDI is a measure of development that combines education, fertility, and poverty. Countries in the middle, lower-middle, or low SDI quintiles were included, with several exceptions. Albania, Bosnia, and Moldova were excluded despite middle SDI status due to geographical discontinuity with other included countries and lack of available survey data. Libya, Malaysia, Panama, and Turkmenistan were included despite higher-middle SDI status to create better geographical continuity. We did not analyse American Samoa, Federated States of Micronesia, Fiji, Kiribati, Marshall Islands, Samoa, Solomon Islands, or Tonga, where no available survey data could be sourced. Analytical steps are described below, and additional details can be found in the  Supplementary Information .

We compiled a database of survey and census datasets that contained geocoding of subnational administrative boundaries or GPS coordinates for sampled clusters. These included datasets from 528 sources (see Supplementary Table 2 ). These sources comprised at least one data source for all but two countries on our list of LMICs: Western Sahara and French Guiana. We chose to exclude these two countries from our analysis; 42 of 105 included countries have only subnational administrative level data. We extracted demographic, education, and sample design variables. The coding of educational attainment varies across survey families. In some surveys, the precise number of years of attainment is not provided, with attainment instead aggregated into categories such as ‘primary completion’ or ‘secondary completion’. In such cases, individuals who report ‘primary completion’ may have gone on to complete some portion of secondary education, but these additional years of education are not captured in the underlying dataset. Previous efforts to examine trends in mean years of education have either assumed that no additional years of education were completed (that is, primary education only) or have used the midpoint between primary and secondary education as a proxy 28 . Trends in the single-year data, however, demonstrate that such assumptions introduce bias in the estimation of attainment trends over time and space, as differences in actual drop-out patterns or binning schema can lead to biased mean estimates 29 .

For this analysis, we used a recently developed method that selects a training subset of similar surveys across time and space to estimate the unobserved single-year distribution of binned datasets 29 . In comprehensive tests of cross-validation that leveraged data for which the single-year distributions are observed, this algorithmic approach significantly reduces bias in summary statistics estimated from datasets with binned coding schemes compared to alternatives such as the standard-duration method 28 . The years in all coding schemes were mapped to the country- and year-specific references in the UNESCO International Standard Classification of Education (ISCED) for comparability 30 . We used a top coding of 18 years on all data; this is a common threshold in many surveys that have a cap and it is reasonable to assume that the importance of education for health outcomes (and other related SDGs) greatly diminishes after what is the equivalent of 2 to 3 years of graduate education in most systems.

Data were aggregated to mean years of education attained and the proportions achieving key levels of education. The levels chosen were proportion with zero years, proportion with less than primary school (1–5 years of education), proportion with at least primary school (6–11 years of education), and proportion achieving secondary school or higher (12 or more years of education). A subset of the data for a smaller age bin (20–24 years) was also examined to more closely track temporal shifts. Equivalent age bins were aggregated for both women and men to examine disparities in mean years of attainment by sex. Where GPS coordinates were available, data were aggregated to a specific latitude and longitude assuming a simple-random sample, as the cluster is the primary sampling unit for the stratified design survey families, such as the Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS). Where only geographical information was available at the level of administrative units, data were aggregated with appropriate weighting according to their sample design. Design effects were estimated using a package for analysing complex survey data in R 31 .

Spatial covariates

To leverage strength from locations with observations to the entire spatiotemporal domain, we compiled several 5 × 5-km 2 raster layers of possible socioeconomic and environmental correlates of education (Supplementary Table 5 and Supplementary Fig. 6 ). Acquisition of temporally dynamic datasets, where possible, was prioritized to best match our observations and thus predict the changing dynamics of educational attainment. We included nine covariates indexed at the 5 × 5-km 2 level: access to roads, nighttime lights tv , population tv , growing season, aridity tv , elevation, urbanicity tv , irrigation, and year tv (tv, time-varying covariates). More details, including plots of all covariates, can be found in the  Supplementary Information .

Our primary goal is to provide educational attainment predictions across LMICs at a high (local) resolution, and our methods provide the best out-of-sample predictive performance at the expense of inferential understanding. To select covariates and capture possible nonlinear effects and complex interactions between them, an ensemble covariate modelling method was implemented 32 . For each region, three submodels were fitted to our outcomes using all of our covariate data: generalized additive models, boosted regression trees, and lasso regression. Each submodel was fit using fivefold cross-validation to avoid overfitting and the out-of-sample predictions from across the five folds were compiled into a single comprehensive set of predictions from that model. Additionally, the same submodels were also run using 100% of the data and a full set of in-sample predictions were created. The five sets of out-of-sample submodel predictions were fed into the full geostatistical model as predictors when performing the model fit. The in-sample predictions from the submodels were used as the covariates when generating predictions using the fitted full geostatistical model. This methodology maximizes out-of-sample predictive performance at the expense of the ability to provide statistical inference on the relationships between the predictors and the outcome. A recent study has shown that this ensemble approach can improve predictive validity by up to 25% over an individual model 32 . More details on this approach can be found in the  Supplementary Information .

The primary goal of using the stacking procedure in our analyses was to maximize the predictive power of the raster covariates by capturing the nonlinear effects and complex interactions between covariates to optimize the model performance. It has previously been suggested 32 that the primary purpose of the submodel predictions is to improve the mean function of the Gaussian process. Although we have determined a way to include the uncertainty from two of our submodels (lasso regression and generalized additive models (GAM)), we have not determined a way to include uncertainty from the boosted regression tree (BRT) submodel into our final estimates. Whereas GAM and lasso regression seek to fit a single model that best describes the relationship between response variable and some set of predictors, BRT method fits a large number of relatively simple models for which the predictions are then combined to give robust estimates of the response. Although this feature of the BRT model makes it a powerful tool for analysing complex data, quantifying the relative uncertainty contributed by each simple model as well as uncertainty from the complex interactions of the predictor variables is challenging 33 , 34 . It is worth noting, however, that our out-of-sample validation indicates that the 95% coverage is fairly accurate (for example, closely ranges around 95%) as shown in the figures and table of  Supplementary Information section 4.3.2. This indicates that we are not misrepresenting the uncertainty in our final estimates.

Geostatistical model

Gaussian and binomial data are modelled within a Bayesian hierarchical modelling framework using a spatially and temporally explicit hierarchical generalized linear regression model to fit the mean number years of education attainment and the proportion of the population who achieved key bins of school in 14 regions across all LMICs as defined in the GBD study (Extended Data Fig. 1 ). This means we fit 14 independent models for each indicator (for example, the proportion of women with zero years of schooling). GBD study design sought to create regions on the basis of three primary criteria: epidemiological homogeneity, sociodemographic similarity, and geographical contiguity 27 . Fitting our models by these regions has the advantage of allowing for some non-stationarity and non-isotropy in the spatial error term, compared to if we modelled one spatiotemporal random-effect structure over the entire modelling region of all LMICs.

For each Gaussian indicator, we modelled the mean number of years of attainment in each survey cluster, d . Survey clusters are precisely located by their GPS coordinates and year of observation, which we map to a spatial raster location i at time t . We model the mean number of years of attainment as Gaussian data given fixed precision τ and a scaling parameter s d (defined by the sample size in the observed cluster). As we may have observed multiple data clusters within a given location i at time t , we refer to the mean attainment, μ , within a given cluster d by its indexed location i , and time t as μ i ( d ), t ( d ) .

For each binomial indicator, we modelled the number of individuals at a given attainment level in each survey cluster, d . We observed the number of individuals reporting a given attainment level as binomial count data C d among an observed sample size N d . As we may have observed multiple data clusters within a given location i at time t , we refer to the probability of attaining that level, p , within a given cluster d by its indexed location i and time t as p i ( d ), t ( d ) .

We used a continuation-ratio modelling approach to account for the ordinal data structure of the binomial indicators 35 . To do this, the proportion of the population with zero years of education was modelled using a binomial model. The proportion with less than primary education was modelled as those with less than primary education of those that have more than zero years of education. The same method followed for the proportion of population completing primary education. The proportion achieving secondary school or higher was estimated as the complement of the sum of the three binomial models.

The remaining parameter specification was consistent between all indicators in both binomial and Gaussian models:

For indices d , i , and t , *(index) is the value of * at that index. The probabilities p i , t represent both the annual proportions at the space–time location and the probability that an individual had that level of attainment given that they lived at that particular location. The annual probability p i , t of each indicator (or μ i , t for the mean indicators) was modelled as a linear combination of the three submodels (GAM, BRT, and lasso regression), rasterized covariate values X i , t , a correlated spatiotemporal error term Z i , t , country random effects \({{\epsilon }}_{{\rm{ctr}}(i)}\) with one unstructured country random effect fit for each country in the modelling region and all sharing a common variance parameter, γ 2 , and an independent nugget effect \(\,{{\epsilon }}_{i,t}\) with variance parameter σ 2 . Coefficients β h in the three submodels h  = 1, 2, 3 represent their respective predictive weighting in the mean logit link, while the joint error term Z i , t accounts for residual spatiotemporal autocorrelation between individual data points that remains after accounting for the predictive effect of the submodel covariates, the country-level random effect \({{\epsilon }}_{{\rm{ctr}}(i)}\) , and the nugget independent error term, \(\,{{\epsilon }}_{i,t}\) . The purpose of the country-level random effect is to capture spatially unstructured, unobserved country-specific variables, as there are often sharp discontinuities in educational attainment between adjacent countries due to systematic differences in governance, infrastructure, and social policies.

The residuals Z i , t are modelled as a three-dimensional Gaussian process (GP) in space–time centred at zero and with a covariance matrix constructed from a Kronecker product of spatial and temporal covariance kernels. The spatial covariance Σ space is modelled using an isotropic and stationary Matérn function 36 , and temporal covariance Σ time as an annual autoregressive (AR1) function over the 18 years represented in the model. In the stationary Matérn function, Γ is the Gamma function, K v is the modified Bessel function of order v  > 0,  κ  > 0 is a scaling parameter, D denotes the Euclidean distance, and ω 2 is the marginal variance. The scaling parameter, κ , is defined to be \(\kappa =\sqrt{8v}/\delta \) where δ is a range parameter (which is about the distance for which the covariance function approaches 0.1) and v is a scaling constant, which is set to 2 rather than fit from the data 37 , 38 . This parameter is difficult to reliably fit, as documented by many other analyses 37 , 39 , 40 that set this parameter to 2. The number of rows and the number of columns of the spatial Matérn covariance matrix are equal to the number of spatial mesh points for a given modelling region. In the AR1 function, ρ is the autocorrelation function (ACF), and k and j are points in the time series where | k  −  j | defines the lag. The number of rows and the number of columns of the AR1 covariance matrix are equal to the number of temporal mesh points (18). The number of rows and the number of columns of the space–time covariance matrix, Σ space ⊗ Σ time , for a given modelling region are equal to: the number of spatial mesh points × the number of temporal mesh points.

This approach leveraged the residual correlation structure of the data to more accurately predict estimates for locations with no data, while also propagating the dependence in the data through to uncertainty estimates 41 . The posterior distributions were fit using computationally efficient and accurate approximations in R-integrated nested Laplace approximation (INLA) with the stochastic partial differential equations (SPDE) approximation to the Gaussian process residuals using R project version 3.5.1 42 , 43 , 44 , 45 . The SPDE approach using INLA has been demonstrated elsewhere, including the estimation of health indicators, particulate air matter, and population age structure 10 , 11 , 46 , 47 . Uncertainty intervals were generated from 1,000 draws (that is, statistically plausible candidate maps) 48 created from the posterior-estimated distributions of modelled parameters. Additional details regarding model and estimation processes can be found in the  Supplementary Information .

To transform grid cell-level estimates into a range of information that is useful to a wide constituency of potential users, these estimates were aggregated from the 1,000 candidate maps up to district, provincial, and national levels using 5 × 5-km 2 population data 49 . This aggregation also enabled the calibration of estimates to national GBD estimates for 2000–2017. This was achieved by calculating the ratio of the posterior mean national-level estimate from each candidate map draw in the analysis to the posterior mean national estimates from GBD, and then multiplying each cell in the posterior sample by this ratio. National-level estimates from this analysis with GBD estimates can be found in Supplementary Table 44 .

To illustrate how subnational progress has contributed differentially to national progress (Fig. 3 ), we decomposed the improvement in the national rate of secondary completion since 2000 for each country into the additive contributions of rate changes at the second administrative level, where C is the national secondary rate change, N is the total number of second-level administrative units, c i is the population proportion in administrative unit i , and r i is the rate of secondary attainment in administrative unit i .

Although the model can predict at all locations covered by available raster covariates, all final model outputs for which land cover was classified as ‘barren or sparsely vegetated’ were masked, on the basis of the most recently available Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data (2013), as well as areas in which the total population density was less than 10 individuals per 1 × 1-km 2 pixel in 2015 50 . This step has led to improved understanding when communicating with data specialists and policy-makers.

Model validation

Models were validated using source-stratified fivefold cross-validation. To offer a more stringent analysis by respecting some of the source and spatial correlation in the data, holdout sets were created by combining sets of data sources (for example, entire survey- or census-years). Model performance was summarized by the bias (mean error), total variance (root-mean-square error) and 95% data coverage within prediction intervals, and the correlation between observed data and predictions. All validation metrics were calculated on the predictions from the fivefold cross-validation. Where possible, estimates from these models were compared against other existing estimates. Furthermore, measures of spatial and temporal autocorrelation pre- and post-modelling were examined to verify correct recognition, fitting, and accounting for the complex spatiotemporal correlation structure in the data. All validation procedures and corresponding results are provided in the  Supplementary Information .

Limitations

Our analysis is not without several important limitations. First, almost all data collection tools conflate gender and sex and we therefore do not capture the full distribution of sex or gender separately in our data. We refer throughout to the measurement of ‘gender (in)equality’, following the usage in SDG 5. Second, it is extremely difficult to quantify quality of education on this scale in a comparable way. Quality is ultimately a large part of the SDG agenda and of utmost importance to achieving equity in opportunity for social mobility. However, many studies across diverse low- and middle-income settings have linked attainment, even very low levels, to measurable improvement in maternal and child health 17 . As our analysis highlights with the proportional indicators, there are still many subnational regions across the world where large proportions do not complete primary school. A third limitation is that we are unable to measure or account for migration. A concept note released from the forthcoming Global Education Monitoring Report 2019 focuses on how migration and displacement affects schooling 51 . Our estimates of the modelled outcome, educational attainment for a particular space–time–age–sex, are demonstrated to be statistically unbiased ( Supplementary Information section 4.3); however, interpretation of any change in attainment as a change in the underlying education system could potentially be biased by the effects of migration. It is possible that geographical disparities reflect changes in population composition rather than changes in the underlying infrastructure or education system. Pathways for this change are complex and may be voluntary. Those who manage to receive an education in a low-attainment area may have an increased ability to migrate and choose to do so. This change may also be involuntary, particularly in politically unstable areas where displacement may make geographical changes over time difficult to estimate. A shifting population composition is a general limitation of many longitudinal ecological analyses, but the spatially granular nature of the analyses used here may be more sensitive to the effects of mobile populations.

Our analysis is purely predictive but draws heavily in its motivation from a rich history of literature on the role of education in reducing maternal mortality, improving child health, and increasing human capital. Studies have also demonstrated complex relationships between increased education and a myriad of positive health outcomes, such as HIV risk reductions and spillover effects to other household members 52 , 53 . The vast majority of these studies are associational and recent attempts at causal analyses have provided more-mixed evidence 54 , 55 , 56 . Although causal analyses of education are very difficult and often rely on situational quasi-experiments, associational analyses using the most comprehensive datasets demonstrate consistent support for the connection between education and health 17 , 57 . Looking towards future analyses, it will be important to study patterns of change in these data and how they overlap with distributions of health. Lastly, our estimates cannot be seen as a replacement for proper data collection systems, especially for tracking contemporaneous change. Our analysis of uncertainty at a high-resolution may be used to inform investment in more robust data systems and collection efforts, especially if the ultimate goal is to measure and track progress in the quality of schooling.

Reporting summary

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

Data availability

The findings of this study are supported by data that are available in public online repositories, data that are publicly available upon request from the data provider, and data that are not publicly available owing to restrictions by the data provider, which were used under license for the current study, but may be available from the authors upon reasonable request and permission of the data provider. A detailed table of data sources and availability can be found in Supplementary Table 2 . Interactive visualization tools are available at https://vizhub.healthdata.org/lbd/education . All maps presented in this study are generated by the authors; no permissions are required for publication. Administrative boundaries were retrieved from the Global Administrative Unit Layers (GAUL) dataset, implemented by FAO within the CountrySTAT and Agricultural Market Information System (AMIS) projects 58 . Land cover was retrieved from the online Data Pool, courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota 50 . Lakes were retrieved from the Global Lakes and Wetlands Database (GLWD), courtesy of the World Wildlife Fund and the Center for Environmental Systems Research, University of Kassel 59 , 60 . Populations were retrieved from WorldPop 49 , 61 . All maps were produced using ArcGIS Desktop 10.6.

Code availability

Our study follows the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). All code used for these analyses is available online at http://ghdx.healthdata.org/record/ihme-data/lmic-education-geospatial-estimates-2000-2017 , and at http://github.com/ihmeuw/lbd/tree/edu-lmic-2019 .

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Acknowledgements

This work was primarily supported by grant OPP1132415 from the Bill & Melinda Gates Foundation. N.G. is the recipient of a training grant from the National Institute of Child Health and Human Development (T32 HD-007242-36A1).

Author information

A list of participants and their affiliations appears in the online version of the paper

These authors contributed equally: Nicholas Graetz, Lauren Woyczynski

These authors jointly supervised this work: Emmanuela Gakidou, Simon I. Hay

Authors and Affiliations

Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA

Nicholas Graetz, Lauren Woyczynski, Katherine F. Wilson, Jason B. Hall, Natalia V. Bhattacharjee, Roy Burstein, Michael L. Collison, Michael A. Cork, Farah Daoud, Nicole Davis Weaver, Aniruddha Deshpande, Laura Dwyer-Lindgren, Lucas Earl, Nathaniel J. Henry, Bernardo Hernández Prado, Damaris K. Kinyoki, Aubrey J. Levine, Benjamin K. Mayala, Ali H. Mokdad, Jonathan F. Mosser, Christopher J. L. Murray, David M. Pigott, Robert C. Reiner Jr, Nafis Sadat, Lauren E. Schaeffer, Megan F. Schipp, Amber Sligar, John C. Wilkinson, Emmanuela Gakidou & Simon I. Hay

Department of Population and Family Health, Jimma University, Jimma, Ethiopia

Kalkidan Hassen Abate

Department of Neurology, Cairo University, Cairo, Egypt

Foad Abd-Allah

Department of Medicine, University College Hospital, Ibadan, Nigeria

Oladimeji M. Adebayo

School of Medicine, Cardiff University, Cardiff, UK

Victor Adekanmbi

Department of Community Medicine, Zabol University of Medical Sciences, Zabol, Iran

Mahdi Afshari

School of Community Health Sciences, University of Nevada, Reno, NV, USA

Olufemi Ajumobi

National Malaria Elimination Program, Federal Ministry of Health, Abuja, Nigeria

Duke Global Health Institute, Duke University, Durham, NC, USA

Tomi Akinyemiju

Department of Population Health Sciences, Duke University, Durham, NC, USA

Evidence Based Practice Center, Mayo Clinic Foundation for Medical Education and Research, Rochester, MN, USA

Fares Alahdab

Internal Medicine Department, Washington University in St Louis, St Louis, MO, USA

Ziyad Al-Aly

Clinical Epidemiology Center, VA Saint Louis Health Care System, Department of Veterans Affairs, St Louis, MO, USA

Center for Health Systems Research, National Institute of Public Health, Cuernavaca, Mexico

Jacqueline Elizabeth Alcalde Rabanal & Doris D. V. Ortega-Altamirano

Qazvin University of Medical Sciences, Qazvin, Iran

Mehran Alijanzadeh

Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran

Vahid Alipour, Jalal Arabloo, Samad Azari & Aziz Rezapour

King Saud University, Riyadh, Saudi Arabia

Khalid Altirkawi

Department of Health Management, Policy and Economics, Kerman University of Medical Sciences, Kerman, Iran

Mohammadreza Amiresmaili

Faculty of Medicine, Mansoura University, Mansoura, Egypt

Nahla Hamed Anber

Carol Davila University of Medicine and Pharmacy, Bucharest, Romania

Catalina Liliana Andrei

Social Determinants of Health Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran

Mina Anjomshoa

Department of Health Policy and Administration, University of the Philippines Manila, Manila, The Philippines

Carl Abelardo T. Antonio

Department of Applied Social Sciences, Hong Kong Polytechnic University, Hong Kong, China

School of Health Sciences, Birmingham City University, Birmingham, UK

Olatunde Aremu

Monitoring Evaluation and Operational Research Project, ABT Associates Nepal, Lalitpur, Nepal

Krishna K. Aryal

Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran

Mehran Asadi-Aliabadi & Maziar Moradi-Lakeh

Department of Health Informatics, University of Ha’il, Ha’il, Saudi Arabia

Suleman Atique

Department of Statistics and Econometrics, Bucharest University of Economic Studies, Bucharest, Romania

Marcel Ausloos, Claudiu Herteliu & Adrian Pana

Indian Institute of Public Health, Public Health Foundation of India, Gurugram, India

Ashish Awasthi & Sanjay Zodpey

The Judith Lumley Centre, La Trobe University, Melbourne, Victoria, Australia

Beatriz Paulina Ayala Quintanilla

General Office for Research and Technological Transfer, Peruvian National Institute of Health, Lima, Peru

Public Health Risk Sciences Division, Public Health Agency of Canada, Toronto, Ontario, Canada

Alaa Badawi

Department of Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada

Faculty of Medicine, Alexandria University, Alexandria, Egypt

Joseph Adel Mattar Banoub

School of Psychology, University of Auckland, Auckland, New Zealand

Suzanne Lyn Barker-Collo

Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia

Anthony Barnett & Ester Cerin

Department of Community Medicine, Gandhi Medical College Bhopal, Bhopal, India

Neeraj Bedi

Jazan University, Jazan, Saudi Arabia

Nuffield Department of Population Health, University of Oxford, Oxford, UK

Derrick A. Bennett

Department of Statistical and Computational Genomics, National Institute of Biomedical Genomics, Kalyani, India

Krittika Bhattacharyya

Department of Statistics, University of Calcutta, Kolkata, India

Department of Global Health, Global Institute for Interdisciplinary Studies, Kathmandu, Nepal

Suraj Bhattarai

Centre for Global Child Health, University of Toronto, Toronto, Ontario, Canada

Zulfiqar A. Bhutta

Centre of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan

Social Determinants of Health Research Center, Babol University of Medical Sciences, Babol, Iran

Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Ranica, Italy

Boris Bikbov

Center for Neuroscience, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama, Panama

Gabrielle Britton

School of Public Health and Health Systems, University of Waterloo, Waterloo, Ontario, Canada

Zahid A. Butt

Al Shifa School of Public Health, Al Shifa Trust Eye Hospital, Rawalpindi, Pakistan

Department of Population and Health, Metropolitan Autonomous University, Mexico City, Mexico

Rosario Cárdenas

Institute of Public Health, University of Porto, Porto, Portugal

Félix Carvalho

Applied Molecular Biosciences Unit, University of Porto, Porto, Portugal

Colombian National Health Observatory, National Institute of Health, Bogota, Colombia

Carlos A. Castañeda-Orjuela

Epidemiology and Public Health Evaluation Group, National University of Colombia, Bogota, Colombia

Gorgas Memorial Institute for Health Studies, Panama, Panama

Franz Castro

School of Public Health, University of Hong Kong, Hong Kong, China

Ester Cerin

College of Medicine, National Taiwan University, Taipei, Taiwan

Jung-Chen Chang

Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK

Cyrus Cooper

Department of Rheumatology, University of Oxford, Oxford, UK

Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA

Rajat Das Gupta

James P. Grant School of Public Health, Brac University, Dhaka, Bangladesh

Rajat Das Gupta, Mehedi Hasan & Ipsita Sutradhar

Heidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany

Jan-Walter De Neve, Babak Moazen & Shafiu Mohammed

Department of Global Health and Infection, Brighton and Sussex Medical School, Brighton, UK

Kebede Deribe

School of Public Health, Addis Ababa University, Addis, Ababa, Ethiopia

School of Nutrition, Food Science and Technology, Hawassa University, Hawassa, Ethiopia

Beruk Berhanu Desalegn

Department of Midwifery, Debre Markos University, Debre, Markos, Ethiopia

Melaku Desta

Faculty of Veterinary Medicine and Zootechnics, Autonomous University of Sinaloa, Culiacan Rosales, Mexico

Melaku Desta & Daniel Diaz

Health Research Section, Nepal Health Research Council, Kathmandu, Nepal

Meghnath Dhimal

Center of Complexity Sciences, National Autonomous University of Mexico, Mexico City, Mexico

Daniel Diaz

Department of Midwifery, Debre Berhan University, Debre Berhan, Ethiopia

Mesfin Tadese Dinberu

Deputy of Research and Technology, Ministry of Health and Medical Education, Tehran, Iran

Shirin Djalalinia

United Nations World Food Programme, New Delhi, India

Manisha Dubey

Faculty of Medicine, University of Belgrade, Belgrade, Serbia

Eleonora Dubljanin

Department of Internal Medicine, Bahia School of Medicine and Public Health, Salvador, Brazil

Andre R. Durães

Medical Board, Roberto Santos General Hospital, Salvador, Brazil

Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA

Laura Dwyer-Lindgren, Bernardo Hernández Prado, Damaris K. Kinyoki, Ali H. Mokdad, Christopher J. L. Murray, David M. Pigott, Robert C. Reiner Jr, Benn Sartorius, Emmanuela Gakidou & Simon I. Hay

Epidemiology Department, Florida International University, Miami, FL, USA

Mohammad Ebrahimi Kalan

Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden

Ziad El-Khatib

World Health Programme, Université du Québec en Abitibi-Témiscamingue, Rouyn-Noranda, Quebec, Canada

Center of Communicable Disease Control, Ministry of Health and Medical Education, Tehran, Iran

Babak Eshrati

School of Public Health, Arak University of Medical Sciences, Arak, Iran

Babol University of Medical Sciences, Babol, Iran

Mahbobeh Faramarzi

College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia

Mohammad Fareed

Department of Psychology, Federal University of Sergipe, Sao Cristovao, Brazil

Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

Seyed-Mohammad Fereshtehnejad

Division of Neurology, University of Ottawa, Ottawa, Ontario, Canada

REQUIMTE/LAQV, University of Porto, Porto, Portugal

Eduarda Fernandes

Psychiatry Department, Kaiser Permanente, Fontana, CA, USA

Irina Filip

Department of Health Sciences, A. T. Still University, Mesa, AZ, USA

Department of Population Medicine and Health Services Research, Bielefeld University, Bielefeld, Germany

Florian Fischer

Department of Dermatology, Kobe University, Kobe, Japan

Takeshi Fukumoto

Gene Expression & Regulation Program, The Wistar Institute, Philadelphia, PA, USA

Ramón de la Fuente Muñiz National Institute of Psychiatry, Mexico City, Mexico

Jose A. García

Unit of Academic Primary Care, University of Warwick, Coventry, UK

Paramjit Singh Gill

Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia

Tiffany K. Gill

Nursing and Health Sciences Department, University of Massachusetts Boston, Boston, MA, USA

Philimon N. Gona

Department of Biostatistics and Epidemiology, University of Oklahoma, Oklahoma City, OK, USA

Sameer Vali Gopalani

Department of Health and Social Affairs, Government of the Federated States of Micronesia, Palikir, Federated States of Micronesia

School of Medicine, Boston University, Boston, MA, USA

Ayman Grada

School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia

Yuming Guo & Shanshan Li

Department of Epidemiology and Biostatistics, Zhengzhou University, Zhengzhou, China

Academics and Research Department, Rajasthan University of Health Sciences, Jaipur, India

Rajeev Gupta

Department of Medicine, Mahatma Gandhi University of Medical Sciences & Technology, Jaipur, India

Department of Anthropology, University of Delhi, Delhi, India

Vipin Gupta

Department of Pharmacology, Tehran University of Medical Sciences, Tehran, Iran

Arvin Haj-Mirzaian & Arya Haj-Mirzaian

Obesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Arvin Haj-Mirzaian

Department of Radiology, Johns Hopkins University, Baltimore, MD, USA

Arya Haj-Mirzaian

Department of Family and Community Medicine, Arabian Gulf University, Manama, Bahrain

Randah R. Hamadeh

School of Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates

Samer Hamidi

Department of Public Health, Mizan-Tepi University, Tepi, Ethiopia

Hamid Yimam Hassen & Andualem Henok

Unit of Epidemiology and Social Medicine, University Hospital Antwerp, Antwerp, Belgium

Hamid Yimam Hassen

School of Public Health, Curtin University, Perth, Western Australia, Australia

Delia Hendrie & Ted R. Miller

Department of Pediatrics, University of Texas Austin, Austin, TX, USA

Michael K. Hole

Department of Pharmacology and Therapeutics, Dhaka Medical College, Dhaka, Bangladesh

Naznin Hossain

Department of Pharmacology, Bangladesh Industrial Gases Limited, Tangail, Bangladesh

Department of Computer Engineering, Islamic Azad University, Tehran, Iran

Mehdi Hosseinzadeh

Computer Science Department, University of Human Development, Sulaimaniyah, Iraq

Department of Epidemiology and Health Statistics, Central South University, Changsha, China

Department of Community Medicine, University of Ibadan, Ibadan, Nigeria

Olayinka Stephen Ilesanmi

Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Seyed Sina Naghibi Irvani

Institute for Physical Activity and Nutrition, Deakin University, Burwood, Victoria, Australia

Sheikh Mohammed Shariful Islam

Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia

Department of Epidemiology, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Department of Health Care and Public Health, Sechenov First Moscow State Medical University, Moscow, Russia

Mihajlo Jakovljevic

Department of Community Medicine, Banaras Hindu University, Varanasi, India

Ravi Prakash Jha

Environmental Research Center, Duke Kunshan University, Kunshan, China

Nicholas School of the Environment, Duke University, Durham, NC, USA

Department of Ophthalmology, Heidelberg University, Heidelberg, Germany

Jost B. Jonas

Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing, China

Social Determinants of Health Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran

Zahra Jorjoran Shushtari

Department of Family Medicine and Public Health, University of Opole, Opole, Poland

Jacek Jerzy Jozwiak

Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India

Tanuj Kanchan

Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran

Amir Kasaeian

Pars Advanced and Minimally Invasive Medical Manners Research Center, Iran University of Medical Sciences, Tehran, Iran

Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran

Ali Kazemi Karyani, Meghdad Pirsaheb, Fatemeh Rajati, Satar Rezaei, Ehsan Sadeghi & Kiomars Sharafi

ODeL Campus, University of Nairobi, Nairobi, Kenya

Peter Njenga Keiyoro

CSIR-Indian Institute of Toxicology Research, Council of Scientific & Industrial Research, Lucknow, India

Chandrasekharan Nair Kesavachandran

Department of Public Health, Jordan University of Science and Technology, Irbid, Jordan

Yousef Saleh Khader

Social Determinants of Health Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Morteza Abdullatif Khafaie

Epidemiology and Biostatistics Department, Health Services Academy, Islamabad, Pakistan

Ejaz Ahmad Khan

Department of Medical Parasitology, Cairo University, Cairo, Egypt

Mona M. Khater

Clinical Epidemiology Unit, Lund University, Lund, Sweden

Aliasghar A. Kiadaliri

Research and Data Solutions, Synotech Consultants, Nairobi, Kenya

Daniel N. Kiirithio

School of Medicine, Xiamen University Malaysia, Sepang, Malaysia

Yun Jin Kim

Department of Nutrition, Simmons University, Boston, MA, USA

Ruth W. Kimokoti

School of Health Sciences, Kristiania University College, Oslo, Norway

Independent Consultant, Jakarta, Indonesia

Soewarta Kosen

CIBERSAM, San Juan de Dios Sanitary Park, Sant Boi De Llobregat, Spain

Ai Koyanagi

Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain

Department of Anthropology, Panjab University, Chandigarh, India

Kewal Krishan

Department of Social and Preventive Medicine, University of Montreal, Montreal, Quebec, Canada

Barthelemy Kuate Defo

Department of Demography, University of Montreal, Montreal, Quebec, Canada

Department of Psychiatry, University of Nairobi, Nairobi, Kenya

Manasi Kumar

Division of Psychology and Language Sciences, University College London, London, UK

International Institute for Population Sciences, Mumbai, India

Pushpendra Kumar

Department of Community and Family Medicine, University of Baghdad, Baghdad, Iraq

Faris Hasan Lami

School of Nursing, Hong Kong Polytechnic University, Hong Kong, China

Paul H. Lee

Department of Medical Statistics and Epidemiology, Sun Yat-sen University, Guangzhou, China

Alliance for Improving Health Outcomes Inc, Quezon City, The Philippines

Yu Liao & Jaifred Christian F. Lopez

Department of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Lee-Ling Lim

Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, China

Department of Dentistry, Radboud University, Nijmegen, The Netherlands

Stefan Listl

Section for Translational Health Economics, Heidelberg University Hospital, Heidelberg, Germany

Department of Epidemiology and Biostatistics, University of the Philippines Manila, Manila, The Philippines

Jaifred Christian F. Lopez

Department of Public Health, Trnava University, Trnava, Slovakia

Marek Majdan

Community-Based Participatory-Research Center (CBPR), Tehran University of Medical Sciences, Tehran, Iran

Reza Majdzadeh

Knowledge Utilization Research Center (KURC), Tehran University of Medical Sciences, Tehran, Iran

Department of Primary Care and Public Health, Imperial College London, London, UK

Azeem Majeed & Salman Rawaf

Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran

Reza Malekzadeh, Akram Pourshams, Gholamreza Roshandel, Hamideh Salimzadeh & Sadaf G. Sepanlou

Non-communicable Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

Reza Malekzadeh & Sadaf G. Sepanlou

Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran

Mohammad Ali Mansournia

Campus Caucaia, Federal Institute of Education, Science and Technology of Ceará, Caucaia, Brazil

Francisco Rogerlândio Martins-Melo

Public Health Department, Botho University-Botswana, Gaborone, Botswana

Anthony Masaka

Division of Plastic Surgery, University of Washington, Seattle, WA, USA

Benjamin Ballard Massenburg

Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA

Kala M. Mehta

Peru Country Office, United Nations Population Fund (UNFPA), Lima, Peru

Walter Mendoza

Center for Translation Research and Implementation Science, National Institutes of Health, Bethesda, MD, USA

George A. Mensah

Department of Medicine, University of Cape Town, Cape Town, South Africa

George A. Mensah & Jean Jacques Noubiap

Breast Surgery Unit, Helsinki University Hospital, Helsinki, Finland

Tuomo J. Meretoja

University of Helsinki, Helsinki, Finland

Clinical Microbiology and Parasitology Unit, Dr Zora Profozic Polyclinic, Zagreb, Croatia

Tomislav Mestrovic

University Centre Varazdin, University North, Varazdin, Croatia

Pacific Institute for Research & Evaluation, Calverton, MD, USA

Ted R. Miller

Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India

Global Institute of Public Health (GIPH), Ananthapuri Hospitals and Research Centre, Trivandrum, India

Faculty of Internal Medicine, Kyrgyz State Medical Academy, Bishkek, Kyrgyzstan

Erkin M. Mirrakhimov

Department of Atherosclerosis and Coronary Heart Disease, National Center of Cardiology and Internal Disease, Bishkek, Kyrgyzstan

Institute of Addiction Research (ISFF), Frankfurt University of Applied Sciences, Frankfurt, Germany

Babak Moazen

Department of Food Technology, College of Agriculture, Salahaddin University-Erbil, Erbil, Iraq

Dara K. Mohammad

Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden

Department of Information Technology, University of Human Development, Sulaimaniyah, Iraq

Aso Mohammad Darwesh

Health Systems and Policy Research Unit, Ahmadu Bello University, Zaria, Nigeria

Shafiu Mohammed

Non-communicable Diseases Research Center, Tehran University of Medical Sciences, Tehran, Iran

Farnam Mohebi

Iran National Institute of Health Research, Tehran University of Medical Sciences, Tehran, Iran

Clinical Epidemiology and Public Health Research Unit, Burlo Garofolo Institute for Maternal and Child Health, Trieste, Italy

Lorenzo Monasta & Luca Ronfani

Department of Public Health Medicine, University of Kwazulu-Natal, Durban, South Africa

Yoshan Moodley

Health Sciences Research Center, Mazandaran University of Medical Sciences, Sari, Iran

Mahmood Moosazadeh

Social Determinants of Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran

Ghobad Moradi

Department of Epidemiology and Biostatistics, Kurdistan University of Medical Sciences, Sanandaj, Iran

Department of Mathematical Sciences, University of Bath, Bath, UK

Paula Moraga

International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland, Australia

Lidia Morawska

Department of Surgery, University of Washington, Seattle, WA, USA

Shane Douglas Morrison

Department of Health Management and Economics, Tehran University of Medical Sciences, Tehran, Iran

Seyyed Meysam Mousavi

Health Management Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran

Department of Pediatric Medicine, Nishtar Medical University, Multan, Pakistan

Ghulam Mustafa

Department of Pediatrics & Pediatric Pulmonology, Institute of Mother & Child Care, Multan, Pakistan

Cancer Research Center, Tehran University of Medical Sciences, Tehran, Iran

Azin Nahvijou

Department of Epidemiology & Biostatistics, Kermanshah University of Medical Sciences, Kermanshah, Iran

Farid Najafi & Yahya Salimi

Suraj Eye Institute, Nagpur, India

Vinay Nangia

Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa

Duduzile Edith Ndwandwe

General Surgery, Carol Davila University of Medicine and Pharmacy Bucharest, Bucharest, Romania

Ionut Negoi

General Surgery, Emergency Hospital of Bucharest, Bucharest, Romania

Anatomy and Embryology, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania

Ruxandra Irina Negoi

Cardiology, Cardio-Aid, Bucharest, Romania

Department of Biological Sciences, University of Embu, Embu, Kenya

Josephine W. Ngunjiri

Institute for Global Health Innovations, Duy Tan University, Hanoi, Vietnam

Cuong Tat Nguyen

Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

Long Hoang Nguyen & Giang Thu Vu

Public Health Department, Universitas Negeri Semarang, Kota Semarang, Indonesia

Dina Nur Anggraini Ningrum

Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei City, Taiwan

Mazandaran University of Medical Sciences, Sari, Iran

Malihe Nourollahpour Shiadeh

Faculty of Medicine & Health Sciences, Stellenbosch University, Cape Town, South Africa

Peter S. Nyasulu

UCIBIO, University of Porto, Porto, Portugal

Felix Akpojene Ogbo

Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada

Andrew T. Olagunju

Department of Psychiatry, University of Lagos, Lagos, Nigeria

Centre for Healthy Start Initiative, Lagos, Nigeria

Bolajoko Olubukunola Olusanya & Jacob Olusegun Olusanya

Department of Pharmacology and Therapeutics, University of Nigeria Nsukka, Enugu, Nigeria

Obinna E. Onwujekwe

Center for Population Health Research, National Institute of Public Health, Cuernavaca, Mexico

Eduardo Ortiz-Panozo

School of Health and Welfare, Jönköping University, Jönköping, Sweden

Division of Mental and Physical Health, Norwegian Institute of Public Health, Bergen, Norway

Simon Øverland

Department of Psychosocial Science, University of Bergen, Bergen, Norway

Department of Respiratory Medicine, Jagadguru Sri Shivarathreeswara Academy of Health Education and Research, Mysore, India

Mahesh P. A.

Health Outcomes, Center for Health Outcomes & Evaluation, Bucharest, Romania

Adrian Pana

Augenpraxis Jonas, Heidelberg University, Heidelberg, Germany

Songhomitra Panda-Jonas

Regional Medical Research Centre, Indian Council of Medical Research, Bhubaneswar, India

Sanghamitra Pati

Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia

George C. Patton

Population Health, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia

Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy

Norberto Perico & Giuseppe Remuzzi

Department of Economics and Business, University of Groningen, Groningen, The Netherlands

Maarten J. Postma

University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

Department of Nephrology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India

Swayam Prakash

Population Studies, International Institute for Population Sciences, Mumbai, India

Non-communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran

Mostafa Qorbani

College of Medicine, University of Central Florida, Orlando, FL, USA

Amir Radfar

College of Graduate Health Sciences, A. T. Still University, Mesa, AZ, USA

Thalassemia and Hemoglobinopathy Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Fakher Rahim

Metabolomics and Genomics Research Center, Tehran University of Medical Sciences, Tehran, Iran

Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran

Vafa Rahimi-Movaghar & Payman Salamati

Department of Public Health and Mortality Studies, International Institute for Population Sciences, Mumbai, India

Mohammad Hifz Ur Rahman

Policy Research Institute, Kathmandu, Nepal

Chhabi Lal Ranabhat

Institute for Poverty Alleviation and International Development, Yonsei University, Wonju, South Korea

WHO Collaborating Centre for Public Health Education and Training, Imperial College London, London, UK

David Laith Rawaf

University College London Hospitals, London, UK

Academic Public Health, Public Health England, London, UK

Salman Rawaf

Translational Health Research Institute, Western Sydney University, Penrith, New South Wales, Australia

Andre M. N. Renzaho

School of Social Sciences and Psychology, Western Sydney University, Penrith, New South Wales, Australia

Research Directorate, Nihon Gakko University, Fernando De La Mora, Paraguay

Carlos Rios-González

Research Direction, Universidad Nacional de Caaguazú, Coronel Oviedo, Paraguay

Department of Clinical Research, Federal University of Uberlândia, Uberlândia, Brazil

Leonardo Roever

Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran

Gholamreza Roshandel

Infectious Diseases and Tropical Medicine Research Center, Babol University of Medical Sciences, Babol, Iran

Ali Rostami

Centro de Investigación Palmira, Agrosavia, Palmira, Colombia

Enrico Rubagotti

Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Kermanshah University of Medical Sciences, Kermanshah, Iran

Yahya Safari

Department of Psychiatry, All India Institute of Medical Sciences, New Delhi, India

Rajesh Sagar

Department of Pathology, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia

Nasir Salam

Social Development and Health Promotion Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran

Yahya Salimi & Moslem Soofi

Department of Entomology, Ain Shams University, Cairo, Egypt

Abdallah M. Samy

Department of Surgery, Marshall University, Huntington, WV, USA

Juan Sanabria

Department of Nutrition and Preventive Medicine, Case Western Reserve University, Cleveland, OH, USA

Institute of Social Medicine, University of Belgrade, Belgrade, Serbia

Milena M. Santric Milicevic

Centre-School of Public Health and Health Management, University of Belgrade, Belgrade, Serbia

Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK

Benn Sartorius

Surgery Department, Hamad Medical Corporation, Doha, Qatar

Brijesh Sathian

Faculty of Health & Social Sciences, Bournemouth University, Bournemouth, UK

University of Alabama at Birmingham, Birmingham, AL, USA

Arundhati R. Sawant

Dr D. Y. Patil University, Pune, India

Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA

David C. Schwebel

Department of Food Science and Nutrition, Jigjiga University, Jigjiga, Ethiopia

Anbissa Muleta Senbeta

Independent Consultant, Karachi, Pakistan

Masood Ali Shaikh

School of Medicine, Dezful University of Medical Sciences, Dezful, Iran

Mehran Shams-Beyranvand

School of Medicine, Alborz University of Medical Sciences, Karaj, Iran

Chronic Diseases (Home Care) Research Center, Hamadan University of Medical Sciences, Hamadan, Iran

Morteza Shamsizadeh

University School of Management and Entrepreneurship, Delhi Technological University, New Delhi, India

Rajesh Sharma

Department of Pulmonary Medicine, Fudan University, Shanghai, China

Centre for Medical Informatics, University of Edinburgh, Edinburgh, UK

Aziz Sheikh

Division of General Internal Medicine, Harvard University, Boston, MA, USA

National Institute of Infectious Diseases, Tokyo, Japan

Mika Shigematsu

Department of Health Education & Promotion, Kermanshah University of Medical Sciences, Kermanshah, Iran

Soraya Siabani

School of Health, University of Technology Sydney, Sydney, New South Wales, Australia

Brasília University, Brasília, Brazil

Dayane Gabriele Alves Silveira

Department of the Health Industrial Complex and Innovation in Health, Federal Ministry of Health, Brasília, Brazil

Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA

Jasvinder A. Singh

Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA

Department of Epidemiology, School of Preventive Oncology, Patna, India

Dhirendra Narain Sinha

Department of Epidemiology, Healis Sekhsaria Institute for Public Health, Mumbai, India

Centre for Fertility and Health, Norwegian Institute of Public Health, Bergen, Norway

Vegard Skirbekk

Department of Pediatrics, King Saud University, Riyadh, Saudi Arabia

Badr Hasan Sobaih & Mohamad-Hani Temsah

Pediatric Department, King Khalid University Hospital, Riyadh, Saudi Arabia

Badr Hasan Sobaih

Hospital Universitario de la Princesa, Autonomous University of Madrid, Madrid, Spain

Joan B. Soriano

Centro de Investigación Biomédica en Red Enfermedades Respiratorias (CIBERES), Madrid, Spain

Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK

Ireneous N. Soyiri

Hull York Medical School, University of Hull, Hull, UK

Division of Community Medicine, International Medical University, Kuala Lumpur, Malaysia

Chandrashekhar T. Sreeramareddy

Department of Nursing, Muhammadiyah University of Surakarta, Kartasura, Indonesia

Agus Sudaryanto

Department of Public Health, China Medical University, Taichung, Taiwan

Department of Community Medicine, Ahmadu Bello University, Zaria, Nigeria

Mu’awiyyah Babale Sufiyan

Neurology Department, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India

Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India

Department of Medicine, University of Valencia, Valencia, Spain

Rafael Tabarés-Seisdedos

Carlos III Health Institute, Biomedical Research Networking Center for Mental Health Network (CIBERSAM), Madrid, Spain

Department of Pediatrics, Hawassa University, Hawassa, Ethiopia

Birkneh Tilahun Tadesse

International Vaccine Institute, Seoul, South Korea

College of Medicine, Alfaisal University, Riyadh, Saudi Arabia

Mohamad-Hani Temsah

Department of Anesthesiology, Perioperative, and Pain Medicine, University of Virginia, Charlottesville, VA, USA

Abdullah Sulieman Terkawi

Department of Anesthesiology, King Farah Medical City, Riyadh, Saudi Arabia

Department of Medical Microbiology, University of Gondar, Gondar, Ethiopia

Belay Tessema

Department of Epidemiology and Biostatistics, University of Gondar, Gondar, Ethiopia

Zemenu Tadesse Tessema

Department of Public Health and Community Medicine, Central University of Kerala, Kasaragod, India

Kavumpurathu Raman Thankappan

Faculty of Health Sciences, Jagiellonian University Medical College, Krakow, Poland

Roman Topor-Madry

The Agency for Health Technology Assessment and Tariff System, Warsaw, Poland

Department of Pathology and Legal Medicine, University of São Paulo, Ribeirão Preto, Brazil

Marcos Roberto Tovani-Palone

Department of Health Economics, Hanoi Medical University, Hanoi, Vietnam

Bach Xuan Tran

Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

Lorainne Tudor Car

Gomal Center of Biochemistry and Biotechnology, Gomal University, Dera Ismail Khan, Pakistan

Irfan Ullah

TB Culture Laboratory, Mufti Mehmood Memorial Teaching Hospital, Dera Ismail Khan, Pakistan

Division of Health Sciences, University of Warwick, Coventry, UK

Olalekan A. Uthman

Argentine Society of Medicine, Ciudad de Buenos Aires, Argentina

Pascual R. Valdez

Velez Sarsfield Hospital, Buenos Aires, Argentina

Psychosocial Injuries Research Center, Ilam University of Medical Sciences, Ilam, Iran

Yousef Veisani

Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy

Francesco S. Violante

Occupational Health Unit, Sant’Orsola Malpighi Hospital, Bologna, Italy

Department of Health Care Administration and Economics, National Research University Higher School of Economics, Moscow, Russia

Vasily Vlassov

Department of Global Health and Population, Harvard University, Boston, MA, USA

Sebastian Vollmer

Department of Economics, University of Göttingen, Göttingen, Germany

Foundation University Medical College, Foundation University Islamabad, Islamabad, Pakistan

Yasir Waheed

Department of Psychiatry, University of São Paulo, São Paulo, Brazil

Yuan-Pang Wang

Institute of Health and Society, University of Oslo, Oslo, Norway

Andrea Sylvia Winkler

Department of Neurology, Technical University of Munich, Munich, Germany

School of Population Health & Environmental Sciences, King’s College London, London, UK

Charles D. A. Wolfe

NIHR Biomedical Research Centre, Guy’s and St Thomas’ Hospital and Kings College London, London, UK

Department of Diabetes and Metabolic Diseases, University of Tokyo, Tokyo, Japan

Tomohide Yamada

Wolkite University, Wolkite, Ethiopia

Alex Yeshaneh

Centre for Suicide Research and Prevention, University of Hong Kong, Hong Kong, China

Department of Social Work and Social Administration, University of Hong Kong, Hong Kong, China

School of Allied Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia

Engida Yisma

Department of Psychopharmacology, National Center of Neurology and Psychiatry, Tokyo, Japan

Naohiro Yonemoto

Health Economics & Finance, Global Health, Jackson State University, Jackson, MS, USA

Mustafa Z. Younis

School of Medicine, Tsinghua University, Peking, China

Prevention of Cardiovascular Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Mahmoud Yousefifard

Global Health Institute, Wuhan University, Wuhan, China

Chuanhua Yu

Department of Epidemiology and Biostatistics, Wuhan University, Wuhan, China

Department of Medicine, Monash University, Melbourne, Victoria, Australia

Sojib Bin Zaman

Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh

George Warren Brown School, Washington University in St Louis, St Louis, MO, USA

Jianrong Zhang

School of Public Health, Wuhan University of Science and Technology, Wuhan, China

Yunquan Zhang

Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan, China

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• , Jianrong Zhang
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•  & Simon I. Hay

Contributions

S.I.H. and N.G. conceived and planned the study. K.W. and J.H. extracted, processed, and geo-positioned the data. L.W. and N.G. carried out the statistical analyses. All authors provided intellectual inputs into aspects of this study. N.G., L.W., J.H., and L.E. prepared figures and tables. N.G. wrote the manuscript with assistance by S.B.M., and all authors contributed to subsequent revisions.

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Correspondence to Simon I. Hay .

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Extended data figures and tables

Extended data fig. 1 modelling regions based on geographical and sdi regions from the gbd..

Modelling regions were defined as follows. Andean South America, Central America and the Caribbean, central sub-Saharan Africa, East Asia, eastern sub-Saharan Africa, Middle East, North Africa, Oceania, Southeast Asia, South Asia, southern sub-Saharan Africa, Central Asia, Tropical South America, and western sub-Saharan Africa. Regions in grey were not included in our models due to high-middle and high SDIs 27 . The map was produced using ArcGIS Desktop 10.6.

Extended Data Fig. 2 Probability that the ratio of men to women aged 20–24 years who attained primary and secondary education is >1 in 2000 and 2017.

a – d , Probability that ratio is >1 (for example, men complete at a higher rate than women) for attaining primary education ( a , b ) and secondary education ( c , d ), aggregated to first administrative-level units in 2000 ( a , c ) and 2017 ( b , d ). Maps were produced using ArcGIS Desktop 10.6.

Extended Data Fig. 3 Average educational attainment and proportion with no primary school at the first administrative level and absolute difference between women and men aged 20–24 years.

a – d , Average educational attainment for women ( a ) and men ( c ) and proportion with no primary school for women ( b ) and men ( d ) aged 20–24 years in 2017. e , f , The absolute difference in average educational attainment between men and women aged 20–24 years in 2017 ( e ) and proportion of individuals with no primary school education ( f ). Maps reflect administrative boundaries, land cover, lakes and population; grey-coloured grid cells were classified as ‘barren or sparsely vegetated’ and had fewer than ten people per 1 × 1-km 2 grid cell 49 , 58 , 59 , 60 , 62 , or were not included in these analyses. Interactive visualization tools are available at https://vizhub.healthdata.org/lbd/education . Maps were produced using ArcGIS Desktop 10.6.

Supplementary information

Supplementary information.

.Guidelines for Accurate and Transparent Health Estimates Reporting Compliance Checklist, Supplementary Discussion, Supplementary Text on data, methods, and covariates, Model descriptions, Supplementary References, Supplementary Sections 4.3 and 4.3.2, and Supplementary Tables 2, 3, and 44.

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Local Burden of Disease Educational Attainment Collaborators. Mapping disparities in education across low- and middle-income countries. Nature 577 , 235–238 (2020). https://doi.org/10.1038/s41586-019-1872-1

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This is why many young people have no access to proper education

COVID-19 has meant millions of children are out of education. Image:  REUTERS/James Akena

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Stay up to date:, education, gender and work.

• Hundreds of millions of children, adolescents and young people have no access to learning and COVID-19 has exacerbated the problem.
• A UNESCO report shows poverty is the main barrier, ahead of other factors like background, identity and ability.
• There have been some positive steps towards greater inclusion, but more work needs to be done.

More than a quarter of a billion children and young people have been “left behind” and are totally excluded from education systems around the world, and the pandemic has made the problem worse, UNESCO’s 2020 Global Education Monitoring Report shows. While most young people in developed countries treat going to school as a given, many of the world’s most vulnerable and disadvantaged face significant obstacles that prevent them from accessing education. The report looks at rates of participation in education in more than 200 countries. The report highlights deep disparities in access, with poverty identified as the main barrier, ahead of other factors including background, identity and ability. Of the countries analysed, fewer than 10% had legislation in place to ensure children and young people were fully included in the education system.

Hundreds of millions not learning Excluding high-income countries in Europe and North America, just 18% of the world’s poorest youth complete secondary school, the report finds. For poor rural young women in at least 20 – mostly sub-Saharan African – countries, few if any complete secondary school.

As the chart shows, 17% (258 million) of the world’s children, adolescents and youth are not in school. In sub-Saharan Africa, it’s 31% of young people. A vast gap in school attendance rates exists both between wealthy and poorer regions, and between richer and poorer households within individual countries. In low- and middle-income countries, children from the wealthiest 20% of households were three times more likely to complete lower secondary school than those from the poorest neighbourhoods, the report says.

Existing inequalities have been heightened during the COVID-19 pandemic

The report estimates that 40% of low- and lower-middle-income countries did not support disadvantaged learners during school shutdowns. “To rise to the challenges of our time, a move towards more inclusive education is imperative,” says Audrey Azoulay, Director-General of UNESCO . “Rethinking the future of education is all the more important following the COVID-19 pandemic, which further widened and put a spotlight on inequalities. Failure to act will hinder the progress of societies.”

Education reset? Aside from poverty, factors including gender, location, ethnicity, religion, sexual orientation and displacement status can play a role in dictating which children have access to schooling and which do not. Left-behind children may live in communities where the need for equality isn’t recognized, or may be denied access to education through prejudices towards certain groups of people, such as migrants, those with disabilities or people with special needs. However, the report has found signs of progress towards inclusion , with some places setting up resource centres for schools, and countries including Malawi, Cuba and Ukraine, thereby helping mainstream schools to accommodate children with special needs.

Efforts are also being made to meet the needs of different learner groups: the Indian state of Odisha has adopted tribal languages in class while Kenya has adapted school curriculums to the nomadic calendar.

The COVID-19 pandemic and recent social and political unrest have created a profound sense of urgency for companies to actively work to tackle inequity.

The Forum's work on Diversity, Equality, Inclusion and Social Justice is driven by the New Economy and Society Platform, which is focused on building prosperous, inclusive and just economies and societies. In addition to its work on economic growth, revival and transformation, work, wages and job creation, and education, skills and learning, the Platform takes an integrated and holistic approach to diversity, equity, inclusion and social justice, and aims to tackle exclusion, bias and discrimination related to race, gender, ability, sexual orientation and all other forms of human diversity.

The Platform produces data, standards and insights, such as the Global Gender Gap Report and the Diversity, Equity and Inclusion 4.0 Toolkit , and drives or supports action initiatives, such as Partnering for Racial Justice in Business , The Valuable 500 – Closing the Disability Inclusion Gap , Hardwiring Gender Parity in the Future of Work , Closing the Gender Gap Country Accelerators , the Partnership for Global LGBTI Equality , the Community of Chief Diversity and Inclusion Officers and the Global Future Council on Equity and Social Justice .

Despite these encouraging signs, the barriers to an inclusive education remain high for many of the world’s young people. While lockdown closures have exacerbated the situation for many, the pandemic also offers a unique chance to rethink our approach to educational inclusion. “COVID-19 has given us a real opportunity to think afresh about our education systems,” says Manos Antoninis, Director of the Global Education Monitoring Report . “But moving to a world that values and welcomes diversity won’t happen overnight. There is an obvious tension between teaching all children under the same roof and creating an environment where students learn best." However, he adds, COVID-19 has showed us that there is a real chance to do things differently, if only we take it.

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Education in SDGs: What is Inclusive and Equitable Quality Education?

• Open Access
• First Online: 09 December 2022

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• Yoshiko Tonegawa 4  

Part of the book series: Sustainable Development Goals Series ((SDGS))

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Education was positioned as Goal 4 (i.e., SDG4) in SDGs. SDG4 aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.” The lack of education and the inability to read and write often limit access to information and disadvantage the livelihoods of people. From the perspective of acquiring knowledge and skills, along with developing human resources, it is clear that the elements of education are present across all 17 SDGs. In other words, education is a cross-cutting discipline that influences a variety of areas (Kitamura et al. 2014 ) and, as such, plays an important role in achieving all SDGs. The main objective of this chapter is to examine “inclusive and equitable quality education.” First, this chapter provides a brief overview of international trends in educational cooperation from 1945 to 2015, covering the Education for All (EFA), Millennium Development Goals (MDGs), and SDGs. It then discusses “inclusive and equitable quality education,” the core of SDG4. It specifically addresses discussions on equity, inclusion, and the quality of education. Furthermore, the quality of education is examined from four perspectives: school environment, educational attainment, learning achievement, and non-cognitive skills. This chapter also presents the case of education for children with disabilities in Ethiopia.

[This chapter was written by modifying the following chapter: Tonegawa Y ( 2018 ) “Kokusai kyouiku kyouryoku (International cooperation in education)” in Yamada M (ed) Atarashii kokusai kyouryoku ron (New international cooperation theory), Revised edition, Akashi Shoten, Tokyo]

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Is the UN’s Quality Education Goal for Tertiary Level (SDG-4.3) at Stake Due to the Covid-19 Pandemic?

The SDGs and African Higher Education

COVID-19 and Its Influence on Sustainable Development Goal 4: Latin America and Caribbean Region

• Inclusive education
• Quality education
• Education for children with disabilities

1 Introduction

This chapter examines SDG4, which focuses on education. Today, approximately 258 million children and youths are not enrolled in school (UIS 2021b ), and around 773 million adults are illiterate (UIS 2021a ). Being unable to receive adequate education and thus having poor literacy levels (i.e., an inadequate ability to read and write), often mean that individuals are limited in their channels of obtaining information. This causes general disadvantages in daily life. If these individuals can gain education, it is possible to enrich and enhance their livelihoods. Nobel laureate Amartya Sen also states that “widening the coverage and effectiveness of basic education can have a powerful preventive role in reducing human insecurity of nearly every kind” (Sen 2003 ). Therefore, all 17 Sustainable Development Goals (SDGs) involve elements of education, including knowledge, skills attainment, and human resource development, as discussed later.

In this chapter, Sect.  4.2 provides an overview of international trends in educational cooperation. Section  4.3 discusses “inclusive and equitable quality education,” which forms the core of the UN’s Sustainable Development Goal 4 (SDG4)—the primary SDG related to education. This chapter also briefly introduces the case of education for children with disabilities in Ethiopia, based on the discussion of equity, inclusion, and quality of education.

2 International Trends in Educational Cooperation

2.1 educational cooperation in the postwar era.

This section provides an overview of international trends in educational cooperation after World War II. The right to education was articulated in the Universal Declaration of Human Rights adopted by the UN General Assembly in 1948. Specifically, Article 26 states “[e]veryone has the right to education. Education shall be free, at least in the elementary and fundamental stages. Elementary education shall be compulsory” (UN n.d.). This declaration has led to an international consensus that education should be seen as a fundamental human right (Yoshikawa 2010 ).

In the early 1960s, after most colonized nations gained their independence, the United Nations Educational, Scientific, and Cultural Organization (UNESCO) held its first International Conference on Education. At this conference, education ministers from each region gathered to formulate an action plan for education (Kuroda 2016a , b ). Among the goals established at this conference were (1) the eradication of illiteracy, (2) free compulsory education, and (3) Universal Primary Education (UPE).

Similarly, the World Bank also focused on investment efficiency in education and, as a result, expanded education financing in the 1960s. Notably, since the 1980s, when the World Bank highlighted the high rate of return on primary education for society as a whole through its analysis of the rate of return on education, Footnote 1 aid for primary education by developed countries and international organizations accelerated rapidly (Kitamura 2016 ). Demonstrating the impact of education on economic growth has proved a major push to education support provision in low-income countries across the international community that aim for economic development.

2.2 From the 1990s Onward: Education for All (EFA)

2.2.1 world declaration on education for all.

In 1990, the World Conference on Education for All was held in Jomtien, Thailand, as a global conference limited to the field of education. The Conference was led by UNESCO, the World Bank, the United Nations Children’s Fund (UNICEF), and the United Nations Development Programme (UNDP), and culminated in a resolution for the World Declaration on Education for All (the Jomtien Declaration). Following this conference, universal access to basic education was recognized as a goal to be shared by all nations (Kuroda 2016a ). Education for All (EFA) then spread internationally as a slogan related to educational cooperation, and both governments of developed countries and international organizations began to focus on support for EFA. Such focus greatly impacted the education policies in low-income countries.

It should be noted that the Jomtien Declaration differs significantly from the Universal Declaration of Human Rights in that it sets specific numerical targets and target years for the purpose of meeting the goals set therein. For instance, UPE was set to be achieved by 2000, with the aim of halving the illiteracy rate recorded in 1990 (UNESCO 1990 ). By providing such targets, “global governance,” which functions as a framework for the international community to address issues as well as to share awareness of and understand the direction of issues, has been in operation within the education sector since 1990 (Kuroda 2016b ).

2.2.2 The Dakar Framework for Action

In 2000, the World Education Forum was held in Dakar, Senegal. At this Forum, the Dakar Framework for Action was adopted in response to nations’ failure to achieve the aforementioned EFA targets. As part of this action plan, six goals were set; the deadline of 2015 was set specifically for Goals 2, 4, and 5 (see Table 4.1 ). The Forum was attended by more than 1100 participants representing governments, international organizations, and civil society organizations (e.g., NGOs.) from 164 countries (Sifuna and Sawamura 2010 ).

Of particular note is that civil society (e.g., NGOs) began influencing educational cooperation from the 1990s onward (Sifuna and Sawamura 2010 ). For instance, the Global Campaign for Education, whose formation was prompted by the Forum, is a coalition of global organizations of teachers’ unions and NGOs working in the field of education (Miyake 2016 ). This campaign has now grown into a fully-fledged organization comprising membership organizations from over 100 countries. Specifically, the campaign works to ensure that all children have the right to quality education (Nishimura and Sasaoka 2016 ; Global Campaign for Education 2018 ).

Similarly, at the behest of the World Bank, the Fast Track Initiative (FTI) was established in 2002 (renamed the Global Partnership for Education (GPE) in 2011) as a means of promoting financial cooperation aimed at supporting UPE up to 2015. While the FTI initially only provided concentrated support by limiting the number of recipient nations (Kobayashi and Kitamura 2008 ), the GPE emphasizes international partnerships by incorporating all the least developed countries as recipients.

Furthermore, the GPE aims to ensure that different actors (e.g., developed countries, international organizations, civil society organizations, and private companies) contribute to the GPE Fund; they must also coordinate and effectively and efficiently use aid resources among one another (including the governments of low-income countries) (Kitamura 2016 ). In 2002, the focus was on securing funds. However, as time progressed, the goal was to achieve high-quality UPE, and from 2015 onward, they have been aiming to realize SDG4 (Kobayashi and Kitamura 2008 ).

Aid coordination through such international partnerships has progressed rapidly since the early 2000s. It is now seen as an approach in which external donors collaboratively work with low-income countries’ governments and provide necessary and consistent support to the country in question (Kitamura 2016 ). Such support is based on consistent education policies and is provided with an awareness of the division of roles. The implementation of balanced aid coordination among different actors is challenging, particularly due to the power relationships that exist between donors and low-income countries, and the ownership and capacity of low-income countries.

2.2.3 Millennium Development Goals (MDGs)

Among the Millennium Development Goals (MDGs), which were agreed upon at the United Nations (UN) Millennium Summit in 2000, the goals that are related to education are Goal 2, “Achieve Universal Primary Education (UPE),” and Goal 3, “Promote gender equality and empower women” (UN 2008 ). Therefore, both UPE and gender equality in education are further promoted across the globe. While the aforementioned Dakar Framework for Action also focuses on the quality of education, the MDGs place more emphasis on quantity (e.g., school enrollment and access) than quality. Footnote 2 Some criticisms and issues surrounding the MDGs include their lack of perspectives that cannot be measured quantitatively, and the levels of reliability of the data gained from low-income countries. However, both governments of developed countries and aid agencies have focused their efforts on educational cooperation in a bid to achieve the MDGs.

2.2.4 Progress and Evaluation of International Cooperation in Education up to 2015

The achievement deadline of the Dakar Framework for Action and MDGs was set for 2015, at which time these initiatives’ achievements were evaluated. The United Nations Millennium Development Goals Report 2015 gave relatively high marks to education (UN 2015 ). For instance, enrollment rates in low-income countries were found to have increased for both boys and girls, and the reduction of gender disparities in enrollment at the level of primary and secondary education was also mentioned (UN 2015 ). Notably, in sub-Saharan Africa, the net enrollment rate in primary education increased significantly from 52% in 1990 to 80% in 2015 (UN 2015 ). In addition, many low-income countries implemented compulsory and free primary education as a policy aimed at achieving UPE. According to UNESCO ( 2015 , p. 20), in sub-Saharan Africa, 15 countries were found to have made primary education free, after 2000. Footnote 3 Free education was also observed to have reduced the general cost of education for parents/guardians, thereby further contributing to the increase in enrollment rates in primary education.

However, the report also indicated that the increase in the overall completion rate of the final year of primary education was miniscule and varied significantly across countries (UNESCO 2015 , p. 20; Sifuna and Sawamura 2010 ). In addition, although the school enrollment rate in general has increased since the implementation of the Dakar Framework and MDGs, many children are still left out of the schooling system.

According to later findings by UNESCO, in 2018, there were approximately 59 million primary school-aged children not attending school (UIS 2021b ). It was also found that many children still do not possess basic learning skills, even though they are enrolled in school. An earlier study by Ogawa and Nishimura ( 2015 ), who conducted a survey in four African nations, found that many local residents and parents who had been actively involved in schools before primary education became compulsory and free, began leaving matters related to schooling to government administration, and their attitudes toward schooling became more passive. As shown here, while some progress has been made toward achieving the MDGs, new issues have simultaneously been identified.

2.3 From 2015 Onward: Sustainable Development Goals (SDGs)

In September 2015, when the deadline for achieving the MDGs arrived, the United Nations Sustainable Development Summit was held, and the SDGs were adopted. UNESCO (n.d., 2016 ) states that the elements of education are present across all 17 SDGs from the perspective of acquiring knowledge and skills, along with developing human resources, as shown in Table 4.2 . In other words, education is a cross-cutting discipline that influences a variety of areas (Kitamura et al. 2014 ) and, as such, plays an important role in achieving any given SDG.

Education was positioned as Goal 4 (i.e., SDG4) in SDGs. SDG4 reflects the concept of inclusive education, which not only reflects access to education, but also outlines the quality, equity, and diversity related thereto (see Table 4.3 ). In addition, the 10 targets underpinning SDG4 are divided across various educational fields, from pre-primary education to higher education, vocational training, adult education, gender, and peace education. Although indicators are provided for each target, these differ significantly from the MDGs, in that they include perspectives on the content and quality of education that cannot only be measured quantitatively. However, criticism has been leveled at the SDGs with respect to how, despite having incorporated diverse opinions, these goals are incredibly complex and have included targets that are difficult to monitor as a whole.

3 What is “Inclusive and Equitable Quality Education?”

Thus far, this chapter has reviewed international cooperation in education toward the adoption of SDGs. This section shifts focus to examine “inclusive and equitable quality education,” which forms the core of SDG4. This examination is presented by dividing the section into three main points: equity, inclusion, and quality of education. This section also briefly introduces the case of education for children with disabilities in Ethiopia, based on the discussion of equity, inclusion, and quality of education.

Prior to the adoption of SDGs, the focus had been on correcting gender disparities in school enrollment, and achieving equality in terms of quantity had been emphasized rather than the promotion of equity (Kuroda 2014 ; Nishimura and Sasaoka 2016 ). Nishimura and Sasaoka ( 2016 ) describe the equality and equity of education as follows: equality refers to a state in which all people are equal, while equity refers to the different educational treatment of people in different environments to achieve equality (Nishimura and Sasaoka 2016 ). From the viewpoint of equity, it is justifiable to offer more support to groups who are in a position of disadvantage (Miwa 2005 ). UNESCO ( 2017 ) thus analyzed the equity of education based on items such as gender equality, geographical conditions, income status, language, and disability.

In addition, Schleicher ( 2014 , p. 19) argued that equity in education can be interpreted from two perspectives, namely, fairness/equity and inclusion/inclusiveness. The perspective of fairness refers to education not being restricted by gender, ethnic group, family environment, or other personal or socioeconomic conditions. In turn, equity from an inclusion perspective relates to how all students should acquire at least basic academic skills (Schleicher 2014 ). In other words, equitable education is concerned with helping students develop their potential learning abilities without experiencing any barriers. The interpretation of equity based on these two perspectives is also consistent with the concept of inclusive education, which is discussed in more detail later.

Furthermore, Nishimura and Sasaoka ( 2016 ) compared the definitions of equity established by the four international organizations (i.e., the World Bank, OECD, FTI, and UNESCO) and highlighted that equity has generally been understood at different levels and has, therefore, not been agreed upon internationally. In the future, it would be necessary to pay close attention to how the international community might reach an agreement on the best approach to achieve equity. Currently, however, it is clear that equity is closely related to both the quality and inclusive nature of education. It is therefore important to consider these perspectives comprehensively when considering specific approaches to education.

3.2 Inclusion

3.2.1 inclusive education.

As noted previously, SDG4 includes an inclusion perspective. Inclusion can be defined in a variety of ways. For instance, UNESCO ( 2003 , p. 7) defines inclusion in education as “a process of addressing and responding to the diversity of needs of all learners through increasing participation in learning, cultures and communities, and reducing exclusion within and from education.” In this sense, inclusive education is understood as an educational approach that realizes the concept of inclusive education, which values its process and response to diverse needs.

Inclusive education was internationally proposed in the Salamanca Statement, which was adopted by the World Congress on Special Needs Education (WCSNE) in 1994. Article 2 of this statement presents the basic concept of inclusive education, which can be summarized as “…every child has a fundamental right to education, and regular schools must provide opportunities for education that takes into account the special educational needs that each child has” (UNESCO and Ministry of Education and Science in Spain 1994 , p. viii). The concept of integrated education, which had been the mainstream approach to schooling until the adoption of inclusive education, required that the children with special needs adapt themselves to regular classes. Inclusive education differs from this concept of integrated education in that it supports the notion that teachers and schools should respond to children’s needs (Kawaguchi and Kuroda 2013 ).

Even in the approach to education, up to 2015, which was the aim for UPE by focusing on access to education, there still existed vulnerable children who had difficulties accessing schooling (Kawaguchi and Kuroda 2013 ; Hayashi 2016 ). For instance, children with disabilities, children in minority groups from the perspective of race, ethnicity, and language, children in low-income families, and other children from various backgrounds were found to have different educational needs. Education methods that meet diverse educational needs and address equity are thus an important approach that should be employed to improve the quality of education.

3.3 Quality of Education

The importance of quality learning in the classroom is confirmed by the Dakar Framework for Action, with Goals 2 and 6, including a quality of education perspective (see Table 4.1 ). However, due in part to the impact of the MDGs, the international focus has tended to be predominantly on the quantity of education. Following the MDGs, since there has been some progress with respect to the quantity of education, SDG4 now emphasizes the quality of education. However, the interpretation of what the quality of education entails is diverse. As a more comprehensive interpretation, according to the “EFA Global Monitoring Report 2005,” quality education is based on educational objectives defined in a social context (UNESCO 2004 ). This perspective of addressing the social context is also consistent with the previously presented concepts of equity and inclusive education.

Nishimura ( 2018 , p. 2) further indicated that previous educational cooperation had been centered on the “theory of defects,” which focused on the shortcomings and weaknesses of low-income countries, based on their comparison with developed countries. Rather, Nishimura ( 2018 , p. 2) described the importance of the “theory of context,” which presupposes education in accordance with the context of each individual country and unique society. With respect to such noted differences in theory and approach, the quality of education has characteristics that make it difficult to set indicators and measure growth. This section specifically considers the quality of education from four perspectives: school environment, educational attainment, learning achievement, and non-cognitive skills.

3.3.1 School Environment

The school environment primarily includes the implementation of educational resources, including teachers, textbooks, and school buildings. Within the school environment, the issue of teacher quality has always been of particular importance, and is often discussed with respect to how the quality of teachers greatly affects education (Saito 2008 ). With the noted expansion of access to education and rise in enrollment rates, the number of students in one class has increased, along with the ratio of students to teachers. This increase and subsequent imbalance are due to various factors, including a shortage of teachers, a restricted education budget, and low teacher salaries. In terms of the ability of the teacher, the ratio of qualified teachers is sometimes used as an index. For instance, data regarding teachers with minimum qualifications in primary education indicate significant differences: 61.5% in Ghana (2019), 63.6% in Sierra Leone (2019), and 15.3% in Madagascar (2019) (World Bank 2021 ). Such data highlight that there are countries with insufficient numbers of teachers with minimum qualifications in primary education.

In addition, in many low-income countries, there are issues related to the abilities of teachers. Such issues are often the result of the potentially poor contents of pre-service teacher education at teacher training schools, and/or the absence of in-service training. Teacher training schools in many low-income countries have also been found to contain biases in their taught content and often lack practical training, such as preparing teaching manuals and/or conducting classes (Hamano 2005 ). There are also many cases where in-service training is not institutionalized (Hamano 2005 ).

The physical school environment has also been found to influence the quality of education. There are many instances in low-income countries where teaching materials are scarce and a single textbook has to be shared by several students. Other issues such as scarce school equipment (e.g., desks and chairs), as well as a lack of properly installed toilets and drinking water facilities, have been noted. There is often no electricity available in schools. Various efforts have been made in low-income countries to improve the quality of education based on the school environment.

3.3.2 Educational Attainment

Until the 1980s, the quality of education was mainly measured from a quantitative perspective that considered the degree of educational attainment, along with the school environment. A typical indicator included the completion rate. For instance, in the EFA Development Index, developed by UNESCO to measure the progress of the Dakar Framework for Action, the survival rate from entering primary school to reaching the fifth grade is adopted as an index to measure education quality (UNESCO 2010 ). Indicators related to Goal 2 of the MDGs similarly include “the proportion of students enrolled in Grade 1 who reach the final year of primary education,” and have been used extensively to measure education quality.

In 2019, the global average completion rate of primary education rose to 89.5% (World Bank 2021 ). However, there are many low-income countries where it is common for children to leave primary school for a variety of reasons, including helping with household chores, working to support their families, and/or early marriage. For instance, in 2019, the completion rate of primary education was only 55% in Mozambique, 64% in Benin, and 65% in Burkina Faso (World Bank 2021 ). Such findings indicate that many children are still unable to complete primary school, even if they have been enrolled for primary education.

As noted previously, the completion rate, which is easy to measure as a numerical value, has often been used to measure the quality of education. However, the completion rate is affected by various factors, depending on the country. Such factors include the presence/absence of automatic promotion, as well as the voluntary repetition of a year to obtain higher scores in the final year exam. For instance, in Kenya, students need to score high on their final exam to secure a place in a good secondary school (Sawamura 2006 ). In addition, it has become increasingly clear that some children are not learning, even when they attend school. The limitations of understanding the quality of education based on educational attainment alone have also come to the fore. Against such realities, learning achievement has started to receive more attention.

3.3.3 Learning Achievement

Learning achievement (i.e., academic abilities) began to gain attention from the late 1990s (Miwa 2005 ). A national learning assessment was conducted at each nation’s level to measure learning achievement. Internationally, there also now exist the Trends in International Mathematics and Science Study (TIMSS), conducted by the International Association for the Evaluation of Educational Achievement (IEA), the Progress in International Reading Literacy Study (PIRLS), and the Programme for International Student Assessment (PISA), participated in by the majority of Organisation for Economic Co-operation and Development (OECD) nations.

As noted previously, while the MDGs brought attention to access to education, they also revealed that many students did not acquire basic academic skills, despite attending school. This result was to seek a more comprehensive measurement of learning achievement. A 2017/8 UNESCO report, for instance, found that one-third or less of students met the minimum proficiency levels in mathematics at the end of primary education in Chad, Kuwait, and Nicaragua; less than half of students met the minimum proficiency levels in reading at the end of primary education in Cameroon, Congo, and Togo (UNESCO 2017 , p. 35) In Central and South Asia, 79% of children in their final years of primary school were found to lack the necessary reading comprehension skills (UIS 2017 ).

Similarly, according to a 2017/8 UNESCO report, national learning assessments of reading comprehension and mathematics are being conducted at the end of primary and/or lower secondary education in approximately half of the countries in the world (UNESCO 2017 , p. 34). Furthermore, while international learning assessments, such as the TIMSS and PISA, can be compared among participating countries, some have noted that local and national educational cultures and traditions are often overlooked. Therefore, some leaning assessments are currently being conducted at the local level to reflect the regional characteristics. One example of such assessments is the “Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ),” which is presently being conducted in Eastern and Southern Africa to better measure education quality in these countries. The OECD also developed a “PISA for Development (PISA-D)” for implementation, aimed at low- and middle-income countries (OECD 2020 ).

In addition to measuring learning achievement levels, learning assessments conducted by civil society organizations are being implemented in an attempt to provide feedback to local residents and make recommendations to relevant governments. An NGO located in India, called Pratham, has further formed a partnership with the central government in order to annually assess and publish the educational situation of children living in rural areas (World Bank 2018 , p. 5; Pratham Mumbai Education Initiative 2018 ). Similarly, in Kenya, an NGO called UWEZO conducts a learning assessment based on a household survey (Nishimura 2016 ). Such household survey-based assessments by civil society organizations are also being conducted in other countries. As shown here, learning achievement assessments are conducted not only to ascertain a country’s academic ability state, but also to make concrete improvement measures based on the results while reflecting public opinion.

3.3.4 Non-cognitive Skills

The quality of education that this chapter has detailed thus far has primarily focused on the cognitive skills associated with academic abilities. SDG4 also includes references to non-cognitive skills. Non-cognitive skills relate to skills such as communication, critical thinking, ethics, and citizenship. The OECD ( 2015 , p. 34) calls non-cognitive skills “social and emotional skills,” and presents three skills related to “achieving goals,” “working with others,” and “managing emotions.” Skills for achieving goals comprise perseverance, self-control, and passion for goals; skills for working with others comprise sociability, respect, and caring; skills for managing emotions comprise self-esteem, optimism, and confidence (OECD 2015 , p. 34). Improving non-cognitive skills is an important perspective not only in low-income countries, but also in developed countries.

Furthermore, the global citizenship education and Education for Sustainable Development (ESD) included in SDG4 also correspond to improvements in non-cognitive skills. There has been insufficient agreement on specific educational content pertaining to global citizenship education and ESD. For instance, according to UNESCO ( 2013 , p. 3), global citizenship education aims to “empower learners to engage and assume active roles both locally and globally to face and resolve global challenges and ultimately to become proactive contributors to a more just, peaceful, tolerant, inclusive, secure and sustainable world.” ESD aims to empower “learners to take informed decisions and responsible actions for environmental integrity, economic viability and a just society, for present and future generations, while respecting cultural diversity” (UNESCO 2014 , p. 12).

ESD is further defined as relating to any type of learning or activity that aims to create a sustainable society by perceiving various issues. These include the environment, poverty, human rights, peace, and development as one’s own issues, thereby creating new values and actions that could lead to the resolution of such issues (Ministry of Education, Culture, Sports, Science and Technology in Japan 2013 ). In particular, Japan actively promoted ESD in cooperation with NGOs and advocated for the “United Nations Decade of Education for Sustainable Development (ESD)” at the World Summit on Sustainable Development (Johannesburg Summit) in 2002. In the final year of the “UN Decade of ESD” (i.e., from 2005 to 2014), the UNESCO World Conference on ESD was held in Nagoya City in the Aichi Prefecture, and Okayama City in the Okayama Prefecture of Japan.

ESD includes not only environmental studies but also education for disaster preparedness Footnote 4 (Motoyoshi 2013 , p. 153). Japan experienced many disasters, including the Great Hanshin Earthquake in 1995 and the Great East Japan Earthquake in 2011. Based on these experiences, education for disaster preparedness has been provided in the schools in Japan. As an advanced country in disaster prevention, Japan aims to build a sustainable and resilient society against disasters by sharing its relevant knowledge and technology with the world.

Since the realization of a sustainable society and providing education that reflects the social context are increasingly being sought, many countries now need to improve education for both the cognitive and non-cognitive skills of their citizens. The quality of education is expected to improve through the implementation of education aimed at acquiring and improving non-cognitive skills. However, there are many challenges associated with implementing education related to non-cognitive skills, especially since cognitive skills have been prioritized for so long. For instance, since international learning assessments have emphasized cognitive skills, many countries consider cognitive skills as indicators of a nation’s international competitiveness; education concerning non-cognitive skills has not been adequately provided Footnote 5 (Kitamura et al. 2014 ; Sudo 2010 ). Additionally, with regard to educational systems where entrance examinations are emphasized, cognitive skills have consequently been emphasized, while education pertaining to developing non-cognitive skills has been neglected (World Bank 2018 , p. 5). Determining how and how much education related to non-cognitive skills should be included in a curriculum has been met with various challenges, since such determinations are closely linked to the education policy and system of the country in question.

3.4 Case Study: Education for Children with Disabilities in Ethiopia

Inclusive education is practiced in many low-income countries, often focusing on children with disabilities. Based on the discussion of equity, inclusion, and quality of education discussed thus far, this section introduces the case of education for children with disabilities in Ethiopia.

3.4.1 Overview of Education for Children with Disabilities in Ethiopia

The Federal Democratic Republic of Ethiopia, located in East Africa, implements inclusive education mainly for children with disabilities. The “Special Needs Education Program Strategy” was formed in 2006 and revised in 2012 as the “Special Needs/Inclusive Education Strategy.” Based on this policy, the purpose of special needs/inclusive education in Ethiopia is “[t]o build an inclusive education system which will provide quality, relevant and equitable education and training for all children, youth and adults with special educational needs (SEN) and ultimately enable them to fully participate in the socio-economic development of the country” (Ministry of Education [MoE] in Ethiopia 2012 , p. 12). Therefore, the establishment of an inclusive society is one of the goals of inclusive education in Ethiopia.

The gross enrolment ratio Footnote 6 for primary education (grades 1–8) in Ethiopia is 119.4% (male: 125.1%; female: 113.5%) (UIS 2021c ). The number of children with disabilities enrolled at the primary education level in 2018/19 was 316,271, with a gross enrolment ratio of 11.0% (male: 12.3%; female: 9.7%) Footnote 7 (MoE in Ethiopia 2019). Therefore, the enrollment rates for children with disabilities are significantly lower than those for primary education enrollment in Ethiopia as a whole.

3.4.2 Ethiopia’s Response to the International Approach

In developing inclusive education with respect to the education of children with disabilities, many countries have accommodated children with disabilities in regular schools, as well as closed special schools Footnote 8 (Gedfie and Neggasa 2019 ; NESSE Network of Experts 2012 ; Takahashi et al. 2013 ). Such an approach has been influenced by the adoption of the understanding that “every child has a fundamental right to education, and regular schools must provide opportunities for education that takes into account the special educational needs that each child has,” as set out in Article 2 of the Salamanca Declaration, where the term “regular school” is included within the basic concept of inclusive education (UNESCO and Ministry of Education and Science in Spain 1994 , p. viii). This view of including all children in the same environment also overlaps with the perspective of equality.

Influenced by this international approach, in Ethiopia, special schools/classes are currently transitioning toward regular schools/classes, while regular schools are simultaneously beginning to accept children with disabilities. Specifically, special schools/classes for children with hearing impairments are shifting toward regular schools/classes. In addition to the two schools studied by the author in Addis Ababa, namely, School A and School B, according to interviews with an official from the Ministry of Education, Footnote 9 three special schools for children with hearing impairments in other regions have also shifted in recent years to “regular schools” that accept children without disabilities. This shift suggests that unification between special and regular schools takes place throughout Ethiopia.

School A, which was a special school for children with intellectual disabilities and children with hearing impairments, has become a regular school, and a special class for children with hearing impairments present in this school now also accepts children without disabilities. Footnote 10 Furthermore, School B accepts children without disabilities in special classes for children with hearing impairments, thereby turning these classes into “regular classes”. However, sign language is maintained as the medium of instruction in these “regular classes” at both schools. Many of the students who are enrolled in both schools have disabilities; children without disabilities are in the minority in these classes.

3.4.3 Perceptions of “Regular Classes”

This research revealed that some parents of children without disabilities enrolled in these “regular classes,” have placed importance on the educational environment. For instance, in these “regular classes,” which are former special classes, the number of students in a class is small and, in many cases, an environment is fostered in which teachers can provide adequate consideration to each student. It can further be inferred that for children from economically disadvantaged families who are unable to attend private schools, these “regular classes” provide a place where they can enjoy quality education that better meets their needs.

Conversely, the unification of special schools/classes into regular schools/classes has been criticized. For instance, teachers with hearing impairments have noted that in the current environment, where children with hearing impairments learn alongside hearing children, children with hearing impairments are often unable to sufficiently learn sign language, which is their first language.

This indicates that there are educational needs that cannot be met by unifying special schools/classes into regular schools/classes; there is a demand for special schools/classes in Ethiopia. UNESCO ( 2003 , p. 7) defines inclusive education as “a process of addressing and responding to the diversity of needs of all learners.” To achieve quality education that meets the needs of individual students, it is necessary to continue to explore ways of realizing both an equitable and inclusive education system in its true form, where teachers and schools effectively respond to the needs of students.

4 Concluding Remarks

The first half of this chapter provides an overview of the international trends in educational cooperation. The second half examined “inclusive and equitable quality education,” as included in SDG4.

From the perspective of acquiring knowledge and skills, along with developing human resources, it is clear that the elements of education are present across all 17 SDGs, as shown in Table 4.3 . In other words, education is a cross-cutting discipline that influences a variety of areas (Kitamura et al. 2014 ) and, as such, plays an important role in achieving all SDGs.

The closely related concepts of equity, inclusion, and quality of education reexamine the ways in which education should be conducted for vulnerable children whose education has previously been hindered. This chapter noted that despite inclusive education based on the concept of equity, holding within it many issues related to interpretation and implementation, it is still believed to be able to improve the quality of education and contribute to the development of the full potential of all children. Of further note was that while “inclusive, equitable, and quality education” is unlikely to be uniform, it still needs to be implemented in alignment with the social context of each country. Such an implementation should also take into account the needs of each country’s diverse situations.

The rate of return to schooling equates the value of lifetime earnings of the individual to the net present value of costs of education (Psacharopoulos and Patorinos 2018 , p. 3).

In terms of the quality of education, Goal 2 indicators include “the proportion of students enrolled in Grade 1 who reach the final year of primary education” (UN 2008 ).

The adoption of a free primary education system was widely used in election pledges, as it was easy for voters to understand and for parties to gain support. This system was thus adopted in many countries as a result of domestic political motives (UNESCO 2015 ; Sifuna and Sawamura 2010 ).

Education for disaster preparedness refers to education that focuses on learning how to protect yourself from disasters and other risks present in society (Motoyoshi 2013 , p. 153).

PISA pursues PISA-type academic abilities that focus on problem-solving and that include the degree to which knowledge and skills can be used in various situations in real life (Sudo 2010 ).

Gross enrollment ratio is defined as the “number of students enrolled in a given level of education, regardless of age, expressed as a percentage of the official school-age population corresponding to the same level of education” (UIS n.d.). The net enrollment ratio for primary education in Ethiopia was 87.2% (male: 91.2%; female: 83.2%) in 2020 (UIS 2021c ).

The Ministry of Education in Ethiopia calculates the number of people with disabilities as 15% of the population based on WHO standards.

For instance, Norway (Takahashi et al. 2013 ), Greece (NESSE Network of Experts 2012 ), Italy (NESSE Network of Experts 2012 ), and South Africa (Gedfie and Neggasa 2019 ).

Online interview with a Special Needs Education Official of Ethiopia’s Ministry of Education, August, 2020.

Special classes for children with severe intellectual disabilities as well as children with autism are continued in School A.

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Tonegawa, Y. (2023). Education in SDGs: What is Inclusive and Equitable Quality Education?. In: Urata, S., Kuroda, K., Tonegawa, Y. (eds) Sustainable Development Disciplines for Humanity. Sustainable Development Goals Series. Springer, Singapore. https://doi.org/10.1007/978-981-19-4859-6_4

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An Evaluative Review of Barriers to Critical Thinking in Educational and Real-World Settings

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Though a wide array of definitions and conceptualisations of critical thinking have been offered in the past, further elaboration on some concepts is required, particularly with respect to various factors that may impede an individual’s application of critical thinking, such as in the case of reflective judgment. These barriers include varying levels of epistemological engagement or understanding, issues pertaining to heuristic-based thinking and intuitive judgment, as well as emotional and biased thinking. The aim of this review is to discuss such barriers and evaluate their impact on critical thinking in light of perspectives from research in an effort to reinforce the ‘completeness’ of extant critical thinking frameworks and to enhance the potential benefits of implementation in real-world settings. Recommendations and implications for overcoming such barriers are also discussed and evaluated.

1. Introduction

Critical thinking (CT) is a metacognitive process—consisting of a number of skills and dispositions—that, through purposeful, self-regulatory reflective judgment, increases the chances of producing a logical solution to a problem or a valid conclusion to an argument ( Dwyer 2017 , 2020 ; Dwyer et al. 2012 , 2014 , 2015 , 2016 ; Dwyer and Walsh 2019 ; Quinn et al. 2020 ).

CT has long been identified as a desired outcome of education ( Bezanilla et al. 2019 ; Butler et al. 2012 ; Dwyer 2017 ; Ennis 2018 ), given that it facilitates a more complex understanding of information ( Dwyer et al. 2012 ; Halpern 2014 ), better judgment and decision-making ( Gambrill 2006 ) and less dependence on cognitive bias and heuristic thinking ( Facione and Facione 2001 ; McGuinness 2013 ). A vast body of research (e.g., Dwyer et al. 2012 ; Gadzella 1996 ; Hitchcock 2004 ; Reed and Kromrey 2001 ; Rimiene 2002 ; Solon 2007 ), including various meta-analyses (e.g., Abrami et al. 2008 , 2015 ; Niu et al. 2013 ; Ortiz 2007 ), indicates that CT can be enhanced through targeted, explicit instruction. Though CT can be taught in domain-specific areas, its domain-generality means that it can be taught across disciplines and in relation to real-world scenarios ( Dwyer 2011 , 2017 ; Dwyer and Eigenauer 2017 ; Dwyer et al. 2015 ; Gabennesch 2006 ; Halpern 2014 ). Indeed, the positive outcomes associated with CT transcend educational settings into real-world, everyday situations, which is important because CT is necessary for a variety of social and interpersonal contexts where good decision-making and problem-solving are needed on a daily basis ( Ku 2009 ). However, regardless of domain-specificity or domain-generality of instruction, the transferability of CT application has been an issue in CT research (e.g., see Dumitru 2012 ). This is an important consideration because issues with transferability—for example, in real-world settings—may imply something lacking in CT instruction.

In light of the large, aforementioned body of research focusing on enhancing CT through instruction, a growing body of research has also evaluated the manner in which CT instruction is delivered (e.g., Abrami et al. 2008 , 2015 ; Ahern et al. 2019 ; Cáceres et al. 2020 ; Byerly 2019 ; Dwyer and Eigenauer 2017 ), along with additional considerations for and the barriers to such education, faced by teachers and students alike (e.g., Aliakbari and Sadeghdaghighi 2013 ; Cáceres et al. 2020 ; Cornell et al. 2011 ; Lloyd and Bahr 2010 ; Ma and Liu 2022 ; Ma and Luo 2021 ; Rear 2019 ; Saleh 2019 ); for example, those regarding conceptualisation, beliefs about CT, having feasible time for CT application and CT’s aforementioned transferability. However, there is a significant lack of research investigating barriers to CT application by individuals in real-world settings, even by those who have enjoyed benefits from previous CT instruction. Thus, perhaps the previously conjectured ‘something lacking in CT instruction’ refers to, in conjunction with the teaching of what CT consists of, making clear to students what barriers to CT application we face.

Simply, CT instruction is designed in such a way as to enhance the likelihood of positive decision-making outcomes. However, there are a variety of barriers that can impede an individual’s application of CT, regardless of past instruction with respect to ‘how to conduct CT’. For example, an individual might be regarded as a ‘critical thinker’ because they apply it in a vast majority of appropriate scenarios, but that does not ensure that they apply CT in all such appropriate scenarios. What keeps them from applying CT in those scenarios might well be one of a number of barriers to CT that often go unaddressed in CT instruction, particularly if such instruction is exclusively focused on skills and dispositions. Perhaps too much focus is placed on what educators are teaching their students to do in their CT courses as opposed to what educators should be recommending their students to look out for or advising what they should not be doing. That is, perhaps just as important for understanding what CT is and how it is conducted (i.e., knowing what to do) is a genuine awareness of the various factors and processes that can impede CT; and so, for an individual to think critically, they must know what to look out for and be able to monitor for such barriers to CT application.

To clarify, thought has not changed regarding what CT is or the cognitive/metacognitive processes at its foundation (e.g., see Dwyer 2017 ; Dwyer et al. 2014 ; Ennis 1987 , 1996 , 1998 ; Facione 1990 ; Halpern 2014 ; Paul 1993 ; Paul and Elder 2008 ); rather, additional consideration of issues that have potential to negatively impact CT is required, such as those pertaining to epistemological engagement; intuitive judgment; as well as emotional and biased thinking. This notion has been made clear through what might be perceived of as a ‘loud shout’ for CT over at least the past 10–15 years in light of growing political, economic, social, and health-related concerns (e.g., ‘fake news’, gaps between political views in the general population, various social movements and the COVID-19 pandemic). Indeed, there is a dearth of research on barriers to CT ( Haynes et al. 2016 ; Lloyd and Bahr 2010 ; Mangena and Chabeli 2005 ; Rowe et al. 2015 ). As a result, this evaluative perspective review aims to provide an impetus for updating the manner in which CT education is approached and, perhaps most importantly, applied in real-world settings—through further identifying and elaborating on specific barriers of concern in order to reinforce the ‘completeness’ of extant CT frameworks and to enhance the potential benefits of their implementation 1 .

2. Barriers to Critical Thinking

2.1. inadequate skills and dispositions.

In order to better understand the various barriers to CT that will be discussed, the manner in which CT is conceptualised must first be revisited. Though debate over its definition and what components are necessary to think critically has existed over the 80-plus years since the term’s coining (i.e., Glaser 1941 ), it is generally accepted that CT consists of two main components: skills and dispositions ( Dwyer 2017 ; Dwyer et al. 2012 , 2014 ; Ennis 1996 , 1998 ; Facione 1990 ; Facione et al. 2002 ; Halpern 2014 ; Ku and Ho 2010a ; Perkins and Ritchhart 2004 ; Quinn et al. 2020 ). CT skills—analysis, evaluation, and inference—refer to the higher-order, cognitive, ‘task-based’ processes necessary to conduct CT (e.g., see Dwyer et al. 2014 ; Facione 1990 ). CT dispositions have been described as inclinations, tendencies, or willingness to perform a given thinking skill (e.g., see Dwyer et al. 2016 ; Siegel 1999 ; Valenzuela et al. 2011 ), which may relate to attitudinal and intellectual habits of thinking, as well as motivational processes ( Ennis 1996 ; Norris 1994 ; Paul and Elder 2008 ; Perkins et al. 1993 ; Valenzuela et al. 2011 ). The relationship between CT skills and dispositions has been argued to be mutually dependent. As a result, overemphasising or encouraging the development of one over the other is a barrier to CT as a whole. Though this may seem obvious, it remains the case that CT instruction often places added emphasis on skills simply because they can be taught (though that does not ensure that everyone has or will be taught such skills), whereas dispositions are ‘trickier’ (e.g., see Dwyer 2017 ; Ku and Ho 2010a ). That is, it is unlikely that simply ‘teaching’ students to be motivated towards CT or to value it over short-instructional periods will actually meaningfully enhance it. Moreover, debate exists over how best to train disposition or even measure it. With that, some individuals might be more ‘inherently’ disposed to CT in light of their truth-seeking, open-minded, or inquisitive natures ( Facione and Facione 1992 ; Quinn et al. 2020 ). The barrier, in this context, is how we can enhance the disposition of those who are not ‘inherently’ inclined. For example, though an individual may possess the requisite skills to conduct CT, it does not ensure the tendency or willingness to apply them; and conversely, having the disposition to apply CT does not mean that one has the ability to do so ( Valenzuela et al. 2011 ). Given the pertinence of CT skills and dispositions to the application of CT in a broader sense, inadequacies in either create a barrier to application.

2.2. Epistemological (Mis)Understanding

To reiterate, most extant conceptualisations of CT focus on the tandem working of skills and dispositions, though significantly fewer emphasise the reflective judgment aspect of CT that might govern various associated processes ( Dawson 2008 ; Dwyer 2017 ; Dwyer et al. 2014 , 2015 ; King and Kitchener 1994 , 2004 ; Stanovich and Stanovich 2010 ). Reflective judgment (RJ) refers to a self-regulatory process of decision-making, with respect to taking time to engage one’s understanding of the nature, limits, and certainty of knowing and how this can affect the defense of their reasoning ( Dwyer 2017 ; King and Kitchener 1994 ; Ku and Ho 2010b ). The ability to metacognitively ‘think about thinking’ ( Flavell 1976 ; Ku and Ho 2010b ) in the application of critical thinking skills implies a reflective sensibility consistent with epistemological understanding and the capacity for reflective judgement ( Dwyer et al. 2015 ; King and Kitchener 1994 ). Acknowledging levels of (un)certainty is important in CT because the information a person is presented with (along with that person’s pre-existing knowledge) often provides only a limited source of information from which to draw a conclusion. Thus, RJ is considered a component of CT ( Baril et al. 1998 ; Dwyer et al. 2015 ; Huffman et al. 1991 ) because it allows one to acknowledge that epistemological understanding is necessary for recognising and judging a situation in which CT may be required ( King and Kitchener 1994 ). For example, the interdependence between RJ and CT can be seen in the way that RJ influences the manner in which CT skills like analysis and evaluation are conducted or the balance and perspective within the subsequent inferences drawn ( Dwyer et al. 2015 ; King et al. 1990 ). Moreover, research suggests that RJ development is not a simple function of age or time but more so a function of the amount of active engagement an individual has working in problem spaces that require CT ( Brabeck 1981 ; Dawson 2008 ; Dwyer et al. 2015 ). The more developed one’s RJ, the better able one is to present “a more complex and effective form of justification, providing more inclusive and better integrated assumptions for evaluating and defending a point of view” ( King and Kitchener 1994, p. 13 ).

Despite a lesser focus on RJ, research indicates a positive relationship between it and CT ( Baril et al. 1998 ; Brabeck 1981 ; Dawson 2008 ; Dwyer et al. 2015 ; Huffman et al. 1991 ; King et al. 1990 )—the understanding of which is pertinent to better understanding the foundation to CT barriers. For example, when considering one’s proficiency in CT skills, there might come a time when the individual becomes so good at using them that their application becomes something akin to ‘second nature’ or even ‘automatic’. However, this creates a contradiction: automatic thinking is largely the antithesis of reflective judgment (even though judgment is never fully intuitive or reflective; see Cader et al. 2005 ; Dunwoody et al. 2000 ; Hamm 1988 ; Hammond 1981 , 1996 , 2000 )—those who think critically take their time and reflect on their decision-making; even if the solution/conclusion drawn from the automatic thinking is ‘correct’ or yields a positive outcome, it is not a critically thought out answer, per se. Thus, no matter how skilled one is at applying CT skills, once the application becomes primarily ‘automatic’, the thinking ceases to be critical ( Dwyer 2017 )—a perspective consistent with Dual Process Theory (e.g., Stanovich and West 2000 ). Indeed, RJ acts as System 2 thinking ( Stanovich and West 2000 ): it is slow, careful, conscious, and consistent ( Kahneman 2011 ; Hamm 1988 ); it is associated with high cognitive control, attention, awareness, concentration, and complex computation ( Cader et al. 2005 ; Kahneman 2011 ; Hamm 1988 ); and accounts for epistemological concerns—consistent not only with King and Kitchener’s ( 1994 ) conceptualisation but also Kuhn’s ( 1999 , 2000 ) perspective on metacognition and epistemological knowing . This is where RJ comes into play as an important component of CT—interdependent among the requisite skills and dispositions ( Baril et al. 1998 ; Dwyer et al. 2015 )—it allows one to acknowledge that epistemological understanding is vital to recognising and judging a situation in which CT is required ( King and Kitchener 1994 ). With respect to the importance of epistemological understanding, consider the following examples for elaboration.

The primary goal of CT is to enhance the likelihood of generating reasonable conclusions and/or solutions. Truth-seeking is a CT disposition fundamental to the attainment of this goal ( Dwyer et al. 2016 ; Facione 1990 ; Facione and Facione 1992 ) because if we just applied any old nonsense as justification for our arguments or solutions, they would fail in the application and yield undesirable consequences. Despite what may seem like truth-seeking’s obvious importance in this context, all thinkers succumb to unwarranted assumptions on occasion (i.e., beliefs presumed to be true without adequate justification). It may also seem obvious, in context, that it is important to be able to distinguish facts from beliefs. However, the concepts of ‘fact’ or ‘truth’, with respect to how much empirical support they have to validate them, also require consideration. For example, some might conceptualise truth as factual information or information that has been or can be ‘proven’ true. Likewise, ‘proof’ is often described as evidence establishing a fact or the truth of a statement—indicating a level of absolutism. However, the reality is that we cannot ‘prove’ things—as scientists and researchers well know—we can only disprove them, such as in experimental settings where we observe a significant difference between groups on some measure—we do not prove the hypothesis correct, rather, we disprove the null hypothesis. This is why, in large part, researchers and scientists use cautious language in reporting their results. We know the best our findings can do is reinforce a theory—another concept often misconstrued in the wider population as something like a hypothesis, as opposed to what it actually entails: a robust model for how and/or why a given phenomenon might occur (e.g., gravity). Thus, theories will hold ‘true’ until they are falsified—that is, disproven (e.g., Popper [1934] 1959 , 1999 ).

Unfortunately, ‘proof’, ‘prove’, and ‘proven’—words that ensure certainty to large populations—actually disservice the public in subtle ways that can hinder CT. For example, a company that produces toothpaste might claim its product to be ‘clinically proven’ to whiten teeth. Consumers purchasing that toothpaste are likely to expect to have whiter teeth after use. However, what happens—as often may be the case—if it does not whiten their teeth? The word ‘proven’ implies a false claim in context. Of course, those in research understand that the word’s use is a marketing ploy, given that ‘clinically proven’ sounds more reassuring to consumers than ‘there is evidence to suggest…’; but, by incorrectly using words like ‘proven’ in our daily language, we reinforce a misunderstanding of what it means to assess, measure and evaluate—particularly from a scientific standpoint (e.g., again, see Popper [1934] 1959 , 1999 ).

Though this example may seem like a semantic issue, it has great implications for CT in the population. For example, a vast majority of us grew up being taught the ‘factual’ information that there were nine planets in our solar system; then, in 2006, Pluto was reclassified as a dwarf planet—no longer being considered a ‘major’ planet of our solar system. As a result, we now have eight planets. This change might be perceived in two distinct ways: (1) ‘science is amazing because it’s always developing—we’ve now reached a stage where we know so much about the solar system that we can differentiate celestial bodies to the extent of distinguishing planets from dwarf planets’; and (2) ‘I don’t understand why these scientists even have jobs, they can’t even count planets’. The first perspective is consistent with that of an individual with epistemological understanding and engagement that previous understandings of models and theories can change, not necessarily because they were wrong, but rather because they have been advanced in light of gaining further credible evidence. The second perspective is consistent with that of someone who has failed to engage epistemological understanding, who does not necessarily see that the change might reflect progress, who might be resistant to change, and who might grow in distrust of science and research in light of these changes. The latter point is of great concern in the CT research community because the unwarranted cynicism and distrust of science and research, in context, may simply reflect a lack of epistemological understanding or engagement (e.g., to some extent consistent with the manner in which conspiracy theories are developed, rationalised and maintained (e.g., Swami and Furnham 2014 )). Notably, this should also be of great concern to education departments around the world, as well as society, more broadly speaking.

Upon considering epistemological engagement in more practical, day-to-day scenarios (or perhaps a lack thereof), we begin to see the need for CT in everyday 21st-century life—heightened by the ‘new knowledge economy’, which has resulted in exponential increases in the amount of information made available since the late 1990s (e.g., Darling-Hammond 2008 ; Dwyer 2017 ; Jukes and McCain 2002 ; Varian and Lyman 2003 ). Though increased amounts of and enhanced access to information are largely good things, what is alarming about this is how much of it is misinformation or disinformation ( Commission on Fake News and the Teaching of Critical Literacy in Schools 2018 ). Truth be told, the new knowledge economy is anything but ‘new’ anymore. Perhaps, over the past 10–15 years, there has been an increase in the need for CT above and beyond that seen in the ‘economy’s’ wake—or maybe ever before; for example, in light of the social media boom, political unrest, ‘fake news’, and issues regarding health literacy. The ‘new’ knowledge economy has made it so that knowledge acquisition, on its own, is no longer sufficient for learning—individuals must be able to work with and adapt information through CT in order to apply it appropriately ( Dwyer 2017 ).

Though extant research has addressed the importance of epistemological understanding for CT (e.g., Dwyer et al. 2014 ), it does not address how not engaging it can substantially hinder it—regardless of how skilled or disposed to think critically an individual may be. Notably, this is distinct from ‘inadequacies’ in, say, memory, comprehension, or other ‘lower-order’ cognitively-associated skills required for CT ( Dwyer et al. 2014 ; Halpern 2014 ; see, again, Note 1) in that reflective judgment is essentially a pole on a cognitive continuum (e.g., see Cader et al. 2005 ; Hamm 1988 ; Hammond 1981 , 1996 , 2000 ). Cognitive Continuum Theory postulates a continuum of cognitive processes anchored by reflective judgment and intuitive judgment, which represents how judgment situations or tasks relate to cognition, given that thinking is never purely reflective, nor is it completely intuitive; rather, it rests somewhere in between ( Cader et al. 2005 ; Dunwoody et al. 2000 ). It is also worth noting that, in Cognitive Continuum Theory, neither reflective nor intuitive judgment is assumed, a priori, to be superior ( Dunwoody et al. 2000 ), despite most contemporary research on judgment and decision-making focusing on the strengths of RJ and limitations associated with intuitive judgment ( Cabantous et al. 2010 ; Dhami and Thomson 2012 ; Gilovich et al. 2002 ). Though this point regarding superiority is acknowledged and respected (particularly in non-CT cases where it is advantageous to utilise intuitive judgment), in the context of CT, it is rejected in light of the example above regarding the automaticity of thinking skills.

2.3. Intuitive Judgment

The manner in which human beings think and the evolution of which, over millions of years, is a truly amazing thing. Such evolution has made it so that we can observe a particular event and make complex computations regarding predictions, interpretations, and reactions in less than a second (e.g., Teichert et al. 2014 ). Unfortunately, we have become so good at it that we often over-rely on ‘fast’ thinking and intuitive judgments that we have become ‘cognitively lazy’, given the speed at which we can make decisions with little energy ( Kahneman 2011 ; Simon 1957 ). In the context of CT, this ‘lazy’ thinking is an impediment (as in opposition to reflective judgment). For example, consider a time in which you have been presented numeric data on a topic, and you instantly aligned your perspective with what the ‘numbers indicate’. Of course, numbers do not lie… but people do—that is not to say that the person who initially interpreted and then presented you with those numbers is trying to disinform you; rather, the numbers presented might not tell the full story (i.e., the data are incomplete or inadequate, unbeknownst to the person reporting on them); and thus, there might be alternative interpretations to the data in question. With that, there most certainly are individuals who will wish to persuade you to align with their perspective, which only strengthens the impetus for being aware of intuitive judgment as a barrier. Consider another example: have you ever accidentally insulted someone at work, school, or in a social setting? Was it because the statement you made was based on some kind of assumption or stereotype? It may have been an honest mistake, but if a statement is made based on what one thinks they know, as opposed to what they actually know about the situation—without taking the time to recognise that all situations are unique and that reflection is likely warranted in light of such uncertainty—then it is likely that the schema-based ‘intuitive judgment’ is what is a fault here.

Our ability to construct schemas (i.e., mental frameworks for how we interpret the world) is evolutionarily adaptive in that these scripts allow us to: make quick decisions when necessary and without much effort, such as in moments of impending danger, answer questions in conversation; interpret social situations; or try to stave off cognitive load or decision fatigue ( Baumeister 2003 ; Sweller 2010 ; Vohs et al. 2014 ). To reiterate, research in the field of higher-order thinking often focuses on the failings of intuitive judgment ( Dwyer 2017 ; Hamm 1988 ) as being limited, misapplied, and, sometimes, yielding grossly incorrect responses—thus, leading to faulty reasoning and judgment as a result of systematic biases and errors ( Gilovich et al. 2002 ; Kahneman 2011 ; Kahneman et al. 1982 ; Slovic et al. 1977 ; Tversky and Kahneman 1974 ; in terms of schematic thinking ( Leventhal 1984 ), system 1 thinking ( Stanovich and West 2000 ; Kahneman 2011 ), miserly thinking ( Stanovich 2018 ) or even heuristics ( Kahneman and Frederick 2002 ; Tversky and Kahneman 1974 ). Nevertheless, it remains that such protocols are learned—not just through experience (as discussed below), but often through more ‘academic’ means. For example, consider again the anecdote above about learning to apply CT skills so well that it becomes like ‘second nature’. Such skills become a part of an individual’s ‘mindware’ ( Clark 2001 ; Stanovich 2018 ; Stanovich et al. 2016 ) and, in essence, become heuristics themselves. Though their application requires RJ for them to be CT, it does not mean that the responses yielded will be incorrect.

Moreover, despite the descriptions above, it would be incorrect, and a disservice to readers to imply that RJ is always right and intuitive judgment is always wrong, especially without consideration of the contextual issues—both intuitive and reflective judgments have the potential to be ‘correct’ or ‘incorrect’ with respect to validity, reasonableness or appropriateness. However, it must also be acknowledged that there is a cognitive ‘miserliness’ to depending on intuitive judgment, in which case, the ability to detect and override this dependence ( Stanovich 2018 )—consistent with RJ, is of utmost importance if we care about our decision-making. That is, if we care about our CT (see below for a more detailed discussion), we must ignore the implicit ‘noise’ associated with the intuitive judgment (regardless of whether or not it is ‘correct’) and, instead, apply the necessary RJ to ensure, as best we can, that the conclusion or solution is valid, reasonable or appropriate.

Although, such a recommendation is much easier said than done. One problem with relying on mental shortcuts afforded by intuition and heuristics is that they are largely experience-based protocols. Though that may sound like a positive thing, using ‘experience’ to draw a conclusion in a task that requires CT is erroneous because it essentially acts as ‘research’ based on a sample size of one; and so, ‘findings’ (i.e., one’s conclusion) cannot be generalised to the larger population—in this case, other contexts or problem-spaces ( Dwyer 2017 ). Despite this, we often over-emphasise the importance of experience in two related ways. First, people have a tendency to confuse experience for expertise (e.g., see the Dunning–KrugerEffect (i.e., the tendency for low-skilled individuals to overestimate their ability in tasks relevant to said skill and highly skilled individuals to underestimate their ability in tasks relevant to said skills); see also: ( Kruger and Dunning 1999 ; Mahmood 2016 ), wherein people may not necessarily be expert, rather they may just have a lot of experience completing a task imperfectly or wrong ( Dwyer and Walsh 2019 ; Hammond 1996 ; Kahneman 2011 ). Second, depending on the nature of the topic or problem, people often evaluate experience on par with research evidence (in terms of credibility), given its personalised nature, which is reinforced by self-serving bias(es).

When evaluating topics in domains wherein one lacks expertise, the need for intellectual integrity and humility ( Paul and Elder 2008 ) in their RJ is increased so that the individual may assess what knowledge is required to make a critically considered judgment. However, this is not necessarily a common response to a lack of relevant knowledge, given that when individuals are tasked with decision-making regarding a topic in which they do not possess relevant knowledge, these individuals will generally rely on emotional cues to inform their decision-making (e.g., Kahneman and Frederick 2002 ). Concerns here are not necessarily about the lack of domain-specific knowledge necessary to make an accurate decision, but rather the (1) belief of the individual that they have the knowledge necessary to make a critically thought-out judgment, even when this is not the case—again, akin to the Dunning–Kruger Effect ( Kruger and Dunning 1999 ); or (2) lack of willingness (i.e., disposition) to gain additional, relevant topic knowledge.

One final problem with relying on experience for important decisions, as alluded to above, is that when experience is engaged, it is not necessarily an objective recollection of the procedure. It can be accompanied by the individual’s beliefs, attitudes, and feelings—how that experience is recalled. The manner in which an individual draws on their personal experience, in light of these other factors, is inherently emotion-based and, likewise, biased (e.g., Croskerry et al. 2013 ; Loftus 2017 ; Paul 1993 ).

2.4. Bias and Emotion

Definitions of CT often reflect that it is to be applied to a topic, argument, or problem of importance that the individual cares about ( Dwyer 2017 ). The issue of ‘caring’ is important because it excludes judgment and decision-making in day-to-day scenarios that are not of great importance and do not warrant CT (e.g., ‘what colour pants best match my shirt’ and ‘what to eat for dinner’); again, for example, in an effort to conserve time and cognitive resources (e.g., Baumeister 2003 ; Sweller 2010 ). However, given that ‘importance’ is subjective, it essentially boils down to what one cares about (e.g., issues potentially impactful in one’s personal life; topics of personal importance to the individual; or even problems faced by an individual’s social group or work organisation (in which case, care might be more extrinsically-oriented). This is arguably one of the most difficult issues to resolve in CT application, given its contradictory nature—where it is generally recommended that CT should be conducted void of emotion and bias (as much as it can be possible), at the same time, it is also recommended that it should only be applied to things we care about. As a result, the manner in which care is conceptualised requires consideration. For example, in terms of CT, care can be conceptualised as ‘concern or interest; the attachment of importance to a person, place, object or concept; and serious attention or consideration applied to doing something correctly or to avoid damage or risk’; as opposed to some form of passion (e.g., intense, driving or over-powering feeling or conviction; emotions as distinguished from reason; a strong liking or desire for or devotion to some activity, object or concept). In this light, care could be argued as more of a dispositional or self-regulatory factor than emotional bias; thus, making it useful to CT. Though this distinction is important, the manner in which care is labeled does not lessen the potential for biased emotion to play a role in the thinking process. For example, it has been argued that if one cares about the decision they make or the conclusion they draw, then the individual will do their best to be objective as possible ( Dwyer 2017 ). However, it must also be acknowledged that this may not always be the case or even completely feasible (i.e., how can any decision be fully void of emotional input? )—though one may strive to be as objective as possible, such objectivity is not ensured given that implicit bias may infiltrate their decision-making (e.g., taking assumptions for granted as facts in filling gaps (unknowns) in a given problem-space). Consequently, such implicit biases may be difficult to amend, given that we may not be fully aware of them at play.

With that, explicit biases are just as concerning, despite our awareness of them. For example, the more important an opinion or belief is to an individual, the greater the resistance to changing their mind about it ( Rowe et al. 2015 ), even in light of evidence indicating the contrary ( Tavris and Aronson 2007 ). In some cases, the provision of information that corrects the flawed concept may even ‘backfire’ and reinforce the flawed or debunked stance ( Cook and Lewandowsky 2011 ). This cognitive resistance is an important barrier to CT to consider for obvious reasons—as a process; it acts in direct opposition to RJ, the skill of evaluation, as well as a number of requisite dispositions towards CT, including truth-seeking and open-mindedness (e.g., Dwyer et al. 2014 , 2016 ; Facione 1990 ); and at the same time, yields important real-world impacts (e.g., see Nyhan et al. 2014 ).

The notion of emotion impacting rational thought is by no means a novel concept. A large body of research indicates a negative impact of emotion on decision-making (e.g., Kahneman and Frederick 2002 ; Slovic et al. 2002 ; Strack et al. 1988 ), higher-order cognition ( Anticevic et al. 2011 ; Chuah et al. 2010 ; Denkova et al. 2010 ; Dolcos and McCarthy 2006 ) and cognition, more generally ( Iordan et al. 2013 ; Johnson et al. 2005 ; Most et al. 2005 ; Shackman et al. 2006 ) 2 . However, less attention has specifically focused on emotion’s impact on the application of critical thought. This may be a result of assumptions that if a person is inclined to think critically, then what is yielded will typically be void of emotion—which is true to a certain extent. However, despite the domain generality of CT ( Dwyer 2011 , 2017 ; Dwyer and Eigenauer 2017 ; Dwyer et al. 2015 ; Gabennesch 2006 ; Halpern 2014 ), the likelihood of emotional control during the CT process remains heavily dependent on the topic of application. Consider again, for example; there is no guarantee that an individual who generally applies CT to important topics or situations will do so in all contexts. Indeed, depending on the nature of the topic or the problem faced, an individual’s mindware ( Clark 2001 ; Stanovich 2018 ; Stanovich et al. 2016 ; consistent with the metacognitive nature of CT) and the extent to which a context can evoke emotion in the thinker will influence what and how thinking is applied. As addressed above, if the topic is something to which the individual feels passionate, then it will more likely be a greater challenge for them to remain unbiased and develop a reasonably objective argument or solution.

Notably, self-regulation is an important aspect of both RJ and CT ( Dwyer 2017 ; Dwyer et al. 2014 ), and, in this context, it is difficult not to consider the role emotional intelligence might play in the relationship between affect and CT. For example, though there are a variety of conceptualisations of emotional intelligence (e.g., Bar-On 2006 ; Feyerherm and Rice 2002 ; Goleman 1995 ; Salovey and Mayer 1990 ; Schutte et al. 1998 ), the underlying thread among these is that, similar to the concept of self-regulation, emotional intelligence (EI) refers to the ability to monitor (e.g., perceive, understand and regulate) one’s own feelings, as well as those of others, and to use this information to guide relevant thinking and behaviour. Indeed, extant research indicates that there is a positive association between EI and CT (e.g., Afshar and Rahimi 2014 ; Akbari-Lakeh et al. 2018 ; Ghanizadeh and Moafian 2011 ; Kaya et al. 2017 ; Stedman and Andenoro 2007 ; Yao et al. 2018 ). To shed light upon this relationship, Elder ( 1997 ) addressed the potential link between CT and EI through her description of the latter as a measure of the extent to which affective responses are rationally-based , in which reasonable desires and behaviours emerge from such rationally-based emotions. Though there is extant research on the links between CT and EI, it is recommended that future research further elaborate on this relationship, as well as with other self-regulatory processes, in an effort to further establish the potentially important role that EI might play within CT.

3. Discussion

3.1. interpretations.

Given difficulties in the past regarding the conceptualisation of CT ( Dwyer et al. 2014 ), efforts have been made to be as specific and comprehensive as possible when discussing CT in the literature to ensure clarity and accuracy. However, it has been argued that such efforts have actually added to the complexity of CT’s conceptualisation and had the opposite effect on clarity and, perhaps, more importantly, the accessibility and practical usefulness for educators (and students) not working in the research area. As a result, when asked what CT is, I generally follow up the ‘long definition’, in light of past research, with a much simpler description: CT is akin to ‘playing devil’s advocate’. That is, once a claim is made, one should second-guess it in as many conceivable ways as possible, in a process similar to the Socratic Method. Through asking ‘why’ and conjecturing alternatives, we ask the individual—be it another person or even ourselves—to justify the decision-making. It keeps the thinker ‘honest’, which is particularly useful if we’re questioning ourselves. If we do not have justifiable reason(s) for why we think or intend to act in a particular way (above and beyond considered objections), then it should become obvious that we either missed something or we are biased. It is perhaps this simplified description of CT that gives such impetus for the aim of this review.

Whereas extant frameworks often discuss the importance of CT skills, dispositions, and, to a lesser extent, RJ and other self-regulatory functions of CT, they do so with respect to components of CT or processes that facilitate CT (e.g., motivation, executive functions, and dispositions), without fully encapsulating cognitive processes and other factors that may hinder it (e.g., emotion, bias, intuitive judgment and a lack of epistemological understanding or engagement). With that, this review is neither a criticism of existing CT frameworks nor is it to imply that CT has so many barriers that it cannot be taught well, nor does it claim to be a complete list of processes that can impede CT (see again Note 1). To reiterate, education in CT can yield beneficial effects ( Abrami et al. 2008 , 2015 ; Dwyer 2017 ; Dwyer and Eigenauer 2017 ); however, such efficacy may be further enhanced by presenting students and individuals interested in CT the barriers they are likely to face in its application; explaining how these barriers manifest and operate; and offer potential strategies for overcoming them.

3.2. Further Implications and Future Research

Though the barriers addressed here are by no means new to the arena of research in higher-order cognition, there is a novelty in their collated discussion as impactful barriers in the context of CT, particularly with respect to extant CT research typically focusing on introducing strategies and skills for enhancing CT, rather than identifying ‘preventative measures’ for barriers that can negatively impact CT. Nevertheless, future research is necessary to address how such barriers can be overcome in the context of CT. As addressed above, it is recommended that CT education include discussion of these barriers and encourage self-regulation against them; and, given the vast body of CT research focusing on enhancement through training and education, it seems obvious to make such a recommendation in this context. However, it is also recognised that simply identifying these barriers and encouraging people to engage in RJ and self-regulation to combat them may not suffice. For example, educators might very well succeed in teaching students how to apply CT skills , but just as these educators may not be able to motivate students to use them as often as they might be needed or even to value such skills (such as in attempting to elicit a positive disposition towards CT), it might be the case that without knowing about the impact of the discussed barriers to CT (e.g., emotion and/or intuitive judgment), students may be just as susceptible to biases in their attempts to think critically as others without CT skills. Thus, what such individuals might be applying is not CT at all; rather, just a series of higher-order cognitive skills from a biased or emotion-driven perspective. As a result, a genuine understanding of these barriers is necessary for individuals to appropriately self-regulate their thinking.

Moreover, though the issues of epistemological beliefs, bias, emotion, and intuitive processes are distinct in the manner in which they can impact CT, these do not have set boundaries; thus, an important implication is that they can overlap. For example, epistemological understanding can influence how individuals make decisions in real-world scenarios, such as through intuiting a judgment in social situations (i.e., without considering the nature of the knowledge behind the decision, the manner in which such knowledge interacts [e.g., correlation v. causation], the level of uncertainty regarding both the decision-maker’s personal stance and the available evidence), when a situation might actually require further consideration or even the honest response of ‘I don’t know’. The latter concept—that of simply responding ‘I don’t know’ is interesting to consider because though it seems, on the surface, to be inconsistent with CT and its outcomes, it is commensurate with many of its associated components (e.g., intellectual honesty and humility; see Paul and Elder 2008 ). In the context this example is used, ‘I don’t know’ refers to epistemological understanding. With that, it may also be impacted by bias and emotion. For example, depending on the topic, an individual may be likely to respond ‘I don’t know’ when they do not have the relevant knowledge or evidence to provide a sufficient answer. However, in the event that the topic is something the individual is emotionally invested in or feels passionate about, an opinion or belief may be shared instead of ‘I don’t know’ (e.g., Kahneman and Frederick 2002 ), despite a lack of requisite evidence-based knowledge (e.g., Kruger and Dunning 1999 ). An emotional response based on belief may be motivated in the sense that the individual knows that they do not know for sure and simply uses a belief to support their reasoning as a persuasive tool. On the other hand, the emotional response based on belief might be used simply because the individual may not know that the use of a belief is an insufficient means of supporting their perspective– instead, they might think that their intuitive, belief-based judgment is as good as a piece of empirical evidence; thus, suggesting a lack of empirical understanding. With that, it is fair to say that though epistemological understanding, intuitive judgment, emotion, and bias are distinct concepts, they can influence each other in real-world CT and decision-making. Though there are many more examples of how this might occur, the one presented may further support the recommendation that education can be used to overcome some of the negative effects associated with the barriers presented.

For example, in Ireland, students are not generally taught about academic referencing until they reach third-level education. Anecdotally, I was taught about referencing at age 12 and had to use it all the way through high school when I was growing up in New York. In the context of these referencing lessons, we were taught about the credibility of sources, as well as how analyse and evaluate arguments and subsequently infer conclusions in light of these sources (i.e., CT skills). We were motivated by our teacher to find the ‘truth’ as best we could (i.e., a fundament of CT disposition). Now, I recognise that this experience cannot be generalised to larger populations, given that I am a sample size of one, but I do look upon such education, perhaps, as a kind of transformative learning experience ( Casey 2018 ; King 2009 ; Mezirow 1978 , 1990 ) in the sense that such education might have provided a basis for both CT and epistemological understanding. For CT, we use research to support our positions, hence the importance of referencing. When a ‘reference’ is not available, one must ask if there is actual evidence available to support the proposition. If there is not, one must question the basis for why they think or believe that their stance is correct—that is, where there is logic to the reasoning or if the proposition is simply an emotion- or bias-based intuitive judgment. So, in addition to referencing, the teaching of some form of epistemology—perhaps early in children’s secondary school careers, might benefit students in future efforts to overcome some barriers to CT. Likewise, presenting examples of the observable impact that bias, emotions, and intuitive thought can have on their thinking might also facilitate overcoming these barriers.

As addressed above, it is acknowledged that we may not be able to ‘teach’ people not to be biased or emotionally driven in their thinking because it occurs naturally ( Kahneman 2011 )—regardless of how ‘skilled’ one might be in CT. For example, though research suggests that components of CT, such as disposition, can improve over relatively short periods of time (e.g., over the duration of a semester-long course; Rimiene 2002 ), less is known about how such components have been enhanced (given the difficulty often associated with trying to teach something like disposition ( Dwyer 2017 ); i.e., to reiterate, it is unlikely that simply ‘teaching’ (or telling) students to be motivated towards CT or to value it (or its associated concepts) will actually enhance it over short periods of time (e.g., semester-long training). Nevertheless, it is reasonable to suggest that, in light of such research, educators can encourage dispositional growth and provide opportunities to develop it. Likewise, it is recommended that educators encourage students to be aware of the cognitive barriers discussed and provide chances to engage in CT scenarios where such barriers are likely to play a role, thus, giving students opportunities to acknowledge the barriers and practice overcoming them. Moreover, making students aware of such barriers at younger ages—in a simplified manner, may promote the development of personal perspectives and approaches that are better able to overcome the discussed barriers to CT. This perspective is consistent with research on RJ ( Dwyer et al. 2015 ), in which it was recommended that such enhancement requires not only time to develop (be it over the course of a semester or longer) but is also a function of having increased opportunities to engage CT. In the possibilities described, individuals may learn both to overcome barriers to CT and from the positive outcomes of applying CT; and, perhaps, engage in some form of transformative learning ( Casey 2018 ; King 2009 ; Mezirow 1978 , 1990 ) that facilitates an enhanced ‘valuing’ of and motivation towards CT. For example, through growing an understanding of the nature of epistemology, intuitive-based thinking, emotion, bias, and the manner in which people often succumb to faulty reasoning in light of these, individuals may come to better understand the limits of knowledge, barriers to CT and how both understandings can be applied; thus, growing further appreciation of the process as it is needed.

To reiterate, research suggests that there may be a developmental trajectory above and beyond the parameters of a semester-long training course that is necessary to develop the RJ necessary to think critically and, likewise, engage an adequate epistemological stance and self-regulate against impeding cognitive processes ( Dwyer et al. 2015 ). Though such research suggests that such development may not be an issue of time, but rather the amount of opportunities to engage RJ and CT, there is a dearth of recommendations offered with respect to how this could be performed in practice. Moreover, the how and what regarding ‘opportunities for engagement’ requires further investigation as well. For example, does this require additional academic work outside the classroom in a formal manner, or does it require informal ‘exploration’ of the world of information on one’s own? If the latter, the case of motivational and dispositional levels once again comes into question; thus, even further consideration is needed. One way or another, future research efforts are necessary to identify how best to make individuals aware of barriers to CT, encourage them to self-regulate against them, and identify means of increasing opportunities to engage RJ and CT.

4. Conclusions

Taking heed that it is unnecessary to reinvent the CT wheel ( Eigenauer 2017 ), the aim of this review was to further elaborate on the processes associated with CT and make a valuable contribution to its literature with respect to conceptualisation—not just in light of making people explicitly aware of what it is, but also what it is not and how it can be impeded (e.g., through inadequate CT skills and dispositions; epistemological misunderstanding; intuitive judgment; as well as bias and emotion)—a perspective consistent with that of ‘constructive feedback’ wherein students need to know both what they are doing right and what they are doing wrong. This review further contributes to the CT education literature by identifying the importance of (1) engaging understanding of the nature, limits, and certainty of knowing as individuals traverse the landscape of evidence-bases in their research and ‘truth-seeking’; (2) understanding how emotions and biases can affect CT, regardless of the topic; (3) managing gut-level intuition until RJ has been appropriately engaged; and (4) the manner in which language is used to convey meaning to important and/or abstract concepts (e.g., ‘caring’, ‘proof’, causation/correlation, etc.). Consistent with the perspectives on research advancement presented in this review, it is acknowledged that the issues addressed here may not be complete and may themselves be advanced upon and updated in time; thus, future research is recommended and welcomed to improve and further establish our working conceptualisation of critical thinking, particularly in a real-world application.

Acknowledgments

The author would like to acknowledge, with great thanks and appreciation, John Eigenauer (Taft College) for his consult, review and advice regarding earlier versions of this manuscript.

Funding Statement

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The author declares no conflict of interest.

1 Notably, though inadequacies in cognitive resources (apart from those explicitly set within the conceptualisations of CT discussed; e.g., see Section 2.1 ) are acknowledged as impediments to one’s ability to apply CT (e.g., a lack of relevant background knowledge, as well as broader cognitive abilities and resources ( Dwyer 2017 ; Halpern 2014 ; Stanovich and Stanovich 2010 )), these will not be discussed as focus is largely restricted to issues of cognitive processes that ‘naturally’ act as barriers in their functioning. Moreover, such inadequacies may more so be issues of individual differences than ongoing issues that everyone , regardless of ability, would face in CT (e.g., the impact of emotion and bias). Nevertheless, it is recommended that future research further investigates the influence of such inadequacies in cognitive resources on CT.

2 There is also some research that suggests that emotion may mediate enhanced cognition ( Dolcos et al. 2011 , 2012 ). However, this discrepancy in findings may result from the types of emotion studied—such as task-relevant emotion and task-irrelevant emotion. The distinction between the two is important to consider in terms of, for example, the distinction between one’s general mood and feelings specific unto the topic under consideration. Though mood may play a role in the manner in which CT is conducted (e.g., making judgments about a topic one is passionate about may elicit positive or negative emotions that affect the thinker’s mood in some way), notably, this discussion focuses on task-relevant emotion and associated biases that negatively impact the CT process. This is also an important distinction because an individual may generally think critically about ‘important’ topics, but may fail to do so when faced with a cognitive task that requires CT with which the individual has a strong, emotional perspective (e.g., in terms of passion , as described above).

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Lack of Education: Situation Analysis

Description of the problem, a solution of the problem, ethical consequences of the proposed solution.

The state of the educational system in the modern world is extremely contradictory. At one level, education became one of the most influential spheres in people’s lives, and the number of people who received education is much bigger than in earlier history of humanity. Scientific achievements have become the starting point for many social transformations and scientific and technological progress.

On the other level, the demand for educational services and the high prestige of education as a social institution are accompanied by an increasing number of problems. Some issues are still relevant for many countries: the elimination of illiteracy, the shortage of qualified teachers, the backwardness of educational technologies, the crisis of efficiency and productivity of the educational system. Thus, despite the record number of educated people, the problem of lack of education is more pressing than ever.

In 1869, in his outstanding essay “The New Education,” president of Harvard University Charles Eliot outlined general areas and ways for the education system development. In this essay, Eliot presented strong arguments for the constant renewal of the curriculum and teaching methodology so that learning could keep pace with the development of society. After one and a half hundred years, this approach is still relevant.

Lack of education is the inability of people to acquire specialized skills, such as cognitive skills, socialization, memorization of facts which are necessary for personal development and the development of society and the world economy. It can be manifested in inaccessibility to education for some parts of the population, for such reasons as the lack of schools, teachers, or money to pay for education. It also can be expressed as the education quality of citizens.

Often the inefficiency of the educational process organization has bad result – after several years spent in the educational institution, people cannot find a job as their knowledge and skills are not enough. Lack of education is a social problem, as education should promote humane and productive human life (Costache, 2018). In addition, well-educated people benefit society and continue its development.

Because of the technological development, jobs and competencies change faster than people can adapt. The major part of the world’s population is behind in the most important practical skills. In the nearest future, the major part of jobs will be connected with the IT-sphere. By anticipating changes of this magnitude, companies are urgently trying to find and gain the competencies needed to maintain competitiveness. Skills shortages are now one of the major threats to businesses.

This problem is of global scale and affects all areas of the economy. For example, according to Farooq et al. (2018), the successful economic development of Pakistan requires cooperation with China within the framework of China – Pakistan Economic Corridor (CPEC). However, researchers note a lack of qualified personnel, in particular women, in such areas as higher education and logistics for sufficient fruitful cooperation. The most vulnerable area in which education shortages are unacceptable is health care. However, even this sphere suffers from the problem of unskilled personnel. Coughlin (2017), for example, notes that nurses have not been professional enough for performing their job recently. She explores the field of nursing with Down Syndrome but it can be argued that it applies to all areas of medicine.

Information on the lack of education worldwide, as well as in specific countries, is confirmed by statistics from official sources of international organizations and government think tanks. According to UNESCO Institute for Statistics (2018), for example, one in five children of school age do not attend educational institutions, and the total number of such children in 2016 was more than 260 million. The statistics show that the number of out-of-school children decreased by 114,5 million. Moreover, the gender gap has declined – previously the number of girls not attending school has exceeded the number of boys. It is a reliable source, as this agency has sufficient influence in the world and is entirely independent.

Through statistics, significant gaps in the existing education system can be identified. For example, there is still a serious gap between the life quality in developed and developing countries, which also affects the education level. The publications of Our World in Data, whose research power is located at Oxford University, among many infographics, have also determined literacy rates in world states (Roser & Ortiz-Ospina, 2020). The downside of the provided statistics is that researchers failed to obtain data for several countries. However, these are only a couple of countries with small populations. According to the data, 142 world states have a high level of education of 90-100%, with 8 states having a 100% rate. In just over 20 countries, the literacy rate is below 60%. They found that most African countries had a literacy rate below 30 percent.

To improve the quality and accessibility of education, the modern system must change approaches to methodology in the realities of a contemporary world in which technology rules. Thanks to computers, phones and Internet, students of any age, nationality, and wealth will be able to access world knowledge. The process starts with the introduction of tiered online training, providing flexibility and financial availability.

Innovative technologies can significantly affect the field of higher education. For example, universities can offer students a short program of specialty before he or she is fully engaged in studies. It is a way how disappointments in the chosen area can be avoided. Various tools and opportunities, for example, mobile applications, will allow studying from any place of the world (Camilleri & Camilleri, 2017). It will also favor faster and easier adaptation when students are starting studies after a long break.

Solving the lack of education problem is a common issue. Universities play a significant role in changes and possible reforms. They can organize effective collaboration, followed by the creation of a specific system within which they will share experiences, courses, and questions (Burbules, 2018). The lack of qualified teachers can also be addressed through the availability of technology. After all, within the system, educational institutions can apply to various specialists, not only to theorists but also to practitioners, for help to organize remote education with interested students.

The shortage of personnel around the world is growing, and educational institutions and employers around the world must become partners. Such partnerships between universities and employers aim to ensure that students acquire skills useful in employment. The educational institutions should develop along with labor market and employers’ demands, and the situation is such that education is of unprecedented importance.

Universities must assign qualifications appropriate to the interests of employers. Moreover, employers today are increasingly interested in skills rather than traditional degrees. Higher education institutions should make it easier for students to acquire new skills. Education should not be pumped after graduation – universities should offer students advanced training programs to continue their graduate careers. After all, today, more and more people understand that lifelong learning is the only way to develop. Moreover, technology will be a useful link in these processes.

The introduction of innovative technologies in the educational process affects not only organizational, methodological, or technological aspects but also the value sphere. In a new electronic environment, learning loses its former character. In current conditions, society is increasing demands for personal communicative and professional competences of high qualification specialists. In this case, the loss of influence of the educational institute on the formation of moral qualities of the person can have far-reaching consequences.

The positive ethical consequence that should be noted is education availability. People from different places can reach professors in England or America with one click. A few problems can be allocated from this consequence. The positive one is that in a critical situation, the educational process does not stop. A fresh and bright example is the coordinated work of schools, universities, teachers, and students in the context of the spread of the COVID-19 pandemic in European countries.

Another critical problem is opportunities for self-organization and self-development. New technologies actualize the development of the student’s personal qualities, such as responsibility, autonomy, commitment, initiative. However, a large percentage of those who start studying remotely do not graduate because they have little skill and motivation to learn on their own. The consumer attitude of many students to studying leads to the transfer of responsibility for the process and result of training to the teacher and university.

Experts note that only about a third of students show personal activity in the educational process, most are motivated not to teach independently, but to receive ready information through the teacher. In addition, students’ use of the Internet primarily for public communication and entertainment, rather than as a tool for acquiring knowledge, makes additional problems.

The negative consequence of the introduction the innovative technologies into the educational system is their cost. At first, a large amount of money will be needed to buy the equipment itself. After that, it is a constant expense to maintain the right work, which requires both the attention of specialists and regular expensive updates. It also gives rise to several ethical problems. First, thus education is still not available to all. For example, according to McFarland et al. (2019), not even all children in America have internet access. In addition, the data also vary according to race and metropolitan status:

In 2017, the total percentage of children without home internet was 14. At the same time, the amount of students of nonmetropolitan status is larger than the metropolitan. Data, according to race, show that the most significant percentage of children without Internet access is American Indians and Alaska residents. Further, Afro-Americans and Hispanics are the largest percentages.

While the use of new technologies, and especially the Internet, is intended to make education more accessible, this can only highlight another ethical problem – the gap between rich and poor. This gap creates conflict and crisis situations. It suggests that the struggle of the poorest and middle class for their rights, for a fairer distribution of income, in different forms, will gain strength.

Thus, the number of educated people in the world is steadily increasing, but due to the rapid pace of development of the modern world, the level of education cannot sufficiently meet the demands of society. Schools should teach to think in accordance with the principles of contemporary science and the information and technological realities of modern society. Today, the task of finding a systemic solution designed to create a long-term interaction that will ensure the satisfaction of educational needs and the constant flow of trained personnel into all spheres of industrial relations.

Burbules, N. (2018). Watch IT: The risks and promises of information technologies for education . Routledge.

Camilleri, M. A., & Camilleri, A. C. (2017). Digital learning resources and ubiquitous technologies in education. Technology, Knowledge and Learning , 22 (1), 65-82.

Costache, G. (2018). Lack of education, the main factor in committing anti-social behaviours. Journal of Law and Public Administration , 4 (7), 34-37.

Coughlin, S. (2017). Nurses lack education in caring for patients with down syndrome. The Grace Peterson Nursing Research Colloquium , 24. Web.

Farooq, S., Gul, S., & Khan, M. Z. (2018). Role of trained women workforce in China-Pakistan economic corridor (CPEC): A gender gap analysis. Putaj Humanities & Social Sciences , 25 (1).

McFarland, J., Hussar, B., Zhang, J., Wang, X., Wang, K., Hein, S., Diliberti, M., Forrest Cataldi, E., Bullock Mann, F., & Barmer, A. (2019). The condition of education 2019 (NCES 2019-144). U.S. Department of Education. National Center for Education Statistics. Web.

Roser, M.,& Ortiz-Ospina, E. (2020). Global education . Our World in Data. Web.

UNESCO Institute for Statistics (UIS) (2018). One in five children, adolescents and youth is out of school . UIS fact sheet No. 48. Web.

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ChalkyPapers . "Lack of Education: Situation Analysis." October 10, 2023. https://chalkypapers.com/lack-of-education-situation-analysis/.