research about quality education

Ensuring Quality Education

UNESCO believes that education is a human right for all throughout life and that access must be matched by quality. The Organization is the only United Nations agency with a mandate to cover all aspects of education. It has been entrusted to lead the Global Education 2030 Agenda through Sustainable Development Goal 4.

UNESCO Office in Tashkent with its national partners implements a number of programmes and projects in areas of quality of education, improving curricula and supporting teacher training and the development of teaching materials, inclusive life-learning for all. UNESCO actively cooperates with the Ministries of Education (Ministry of preschool and school education, Ministry of higher education, science and innovation).

UNESCO works with schools to promote the ideals of UNESCO valuing rights and dignity, gender equality, social progress, freedom, justice and democracy, respect for diversity and international solidarity. The UNESCO Associated Schools Network (ASPnet) connects more than 12,000 schools in 182 countries, more than 45 schools in Uzbekistan are connected to this network and implement concrete actions in three priorities: education for sustainable development, global citizenship education and inter-cultural and heritage learning.

UNESCO also cooperates with educational institutions and universities around the world. UNESCO Chairs and UNITWIN Networks involves over 850 institutions in 117 countries, promotes international inter-university cooperation and networking to enhance institutional capacities through knowledge sharing and collaborative work. Eight UNESCO Chairs at seven universities in Uzbekistan are connected to global network to pool their resources, both human and material, to address pressing challenges and contribute to the development of their societies.

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UNICEF Data : Monitoring the situation of children and women

GOAL 4: QUALITY EDUCATION

Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.

Goal 4 aims to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.  This goal supports the reduction of disparities and inequities in education, both in terms of access and quality. It recognizes the need to provide quality education for all, and most especially vulnerable populations, including poor children, children living in rural areas, persons with disabilities, indigenous people and refugee children.

This goal is of critical importance because of its transformative effects on the other SDGs. Sustainable development hinges on every child receiving a quality education. When children are offered the tools to develop to their full potential, they become productive adults ready to give back to their communities and break the cycle of poverty. Education enables upward socioeconomic mobility.

Significant progress was achieved during the last decade in increasing access to education and school enrolment rates at all levels, particularly for girls. Despite these gains, about 260 million children were out of school in 2018, nearly one fifth of the global population in that age group. Furthermore, more than half of all children and adolescents worldwide are failing to meet minimum proficiency standards in reading and mathematics.

UNICEF’s contribution towards reaching this goal centres on equity and inclusion to provide all children with quality learning opportunities and skills development programmes, from early childhood through adolescence. UNICEF works with governments worldwide to raise the quality and inclusiveness of schools.  

UNICEF is custodian for global monitoring of Indicator 4.2.1 Percentage of children (aged 24–59 months) developmentally on track in at least 3 of the 4 following domains: literacy-numeracy, physical, socio-emotional and learning.

Child-related SDG indicators

Target 4.1 by 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomes.

Proportion of children and young people: (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sex

• Indicator definition
• Computation method
• Comments & limitations

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The indicator aims to measure the percentage of children and young people who have achieved the minimum learning outcomes in reading and mathematics during or at the end of the relevant stages of education.

The higher the figure, the higher the proportion of children and/or young people reaching at least minimum proficiency in the respective domain (reading or mathematic) with the limitations indicated under the “Comments and limitations” section.

The indicator is also a direct measure of the learning outcomes achieved in the two subject areas at the end of the relevant stages of education. The three measurement points will have their own established minimum standard. There is only one threshold that divides students into above and below minimum:

Below minimum refers to the proportion or percentage of students who do not achieve a minimum standard as set up by countries according to the globally-defined minimum competencies.

Above minimum refers to the proportion or percentage of students who have achieved the minimum standards. Due to heterogeneity of performance levels set by national and cross-national assessments, these performance levels will have to be mapped to the globally-defined minimum performance levels. Once the performance levels are mapped, the global education community will be able to identify for each country the proportion or percentage of children who achieved minimum standards.

(a) Minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. In September 2018, an agreement was reached on a verbal definition of the global minimum proficiency level of reference for each of the areas and domains of Indicator 4.1.1 as described in the document entitled: Minimum Proficiency Levels (MPLs): Outcomes of the consensus building meeting ( http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/02/MPLs_revised_doc_20190204.docx ).

Minimum proficiency levels (MPLs) defined by each learning assessment to ensure comparability across learning assessments; a verbal definition of MPL for each domain and levels between cross-national assessments (CNAs) were established by conducting an analysis of the performance level descriptors, the descriptions of the performance levels to express the knowledge and skills required to achieve each performance level by domain, of cross-national, regional and community-led tests in reading and mathematics. The analysis was led and completed by the UIS and a consensus among experts on the proposed methodology was deemed adequate and pragmatic.

The global MPL definitions for the domains of reading and mathematics are presented here (insert link)

The Programme for International Student Assessment (PISA) reading test has six proficiency levels, of which Level 2 is described as the minimum proficiency level. In Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study (PIRLS), there are four proficiency levels: Low, Intermediate, High and Advanced. Students reaching the Intermediate benchmark are able to apply basic knowledge in a variety of situations, similar to the idea of minimum proficiency. Currently, there are no common standards validated by the international community or countries. The indicator shows data published by each of the agencies and organizations specialised in cross-national learning assessments.

(a) The number of children and/or young people at the relevant stage of education n in year t achieving at least the pre-defined proficiency level in subject s expressed as a percentage of the number of children and/or young people at stage of education n, in year t, in any proficiency level in subjects.

Harmonize various data sources To address the challenges posed by the limited capacity of some countries to implement cross- national, regional and national assessments, actions have been taken by the UIS and its partners. The strategies are used according to its level of precision and following a reporting protocol ( http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/05/GAML6-WD-2-Protocol-for-reporting-4.1.1_v1.pdf ) that includes the national assessments under specific circumstances.

Out-of-school children In 2016, 263 million children, adolescents and youth were out of school, representing nearly one-fifth of the global population of this age group. 63 million, or 24% of the total, are children of primary school age (typically 6 to 11 years old); 61 million, or 23% of the total, are adolescents of lower secondary school age (typically 12 to 14 years old); and 139 million, or 53% of the total, are youth of upper secondary school age (about 15 to 17 years old). Not all these kids will be permanently outside school, some will re-join the educational system and, eventually, complete late, while some of them will enter late. The quantity varies per country and region and demands some adjustment in the estimate of Indicator 4.1.1. There is currently a discussion on how to implement these adjustments to reflect all the population. In 2017, the UIS proposed to make adjustments using the out-of-school children and the completion rates.( http://uis.unesco.org/en/blog/helping-countries-improve-their-data-out-school-children ) and the completion rates.

Learning outcomes from cross-national learning assessment are directly comparable for all countries which participated in the same cross-national learning assessments. However, these outcomes are not comparable across different cross-national learning assessments or with national learning assessments. A level of comparability of learning outcomes across assessments could be achieved by using different methodologies, each with varying standard errors. The period of 2020-2021 will shed light on the standard errors’ size for these methodologies.

The comparability of learning outcomes over time has additional complications, which require, ideally, to design and implement a set of comparable items as anchors in advance. Methodological developments are underway to address comparability of assessments outcomes over time.

While data from many national assessments are available now, every country sets its own standards so the performance levels might not be comparable. One option is to link existing regional assessments based on a common framework. Furthermore, assessments are typically administered within school systems, the current indicators cover only those in school and the proportion of in-school target populations might vary from country to country due to varied out-of-school children populations. Assessing competencies of children and young people who are out of school would require household-based surveys. Assessing children in households is under consideration but may be very costly and difficult to administer and unlikely to be available on the scale needed within the next 3-5 years. Finally, the calculation of this indicator requires specific information on the ages of children participating in assessments to create globally-comparable data. The ages of children reported by the head of the household might not be consistent and reliable so the calculation of the indicator may be even more challenging. Due to the complication in assessing out-of-school children and the main focus on improving education system, the UIS is taking a stepping stone approach. It will concentrate on assessing children in school in the medium term, where much data are available, then develop more coherent implementation plan to assess out-of-school children in the longer term.

Click on the button below to explore the data behind this indicator.

Completion rate (primary education, lower secondary education, upper secondary education)

A completion rate of 100% indicates that all children and adolescents have completed a level of education by the time they are 3 to 5 years older than the official age of entry into the last grade of that level of education. A low completion rate indicates low or delayed entry into a given level of education, high drop-out, high repetition, late completion, or a combination of these factors.

Percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of each level of education who have completed that grade.

The intended age for the last grade of each level of education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade.

For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate.

The number of persons in the relevant age group who have completed the last grade of a given level of education is divided by the total population (in the survey sample) of the same age group.

The age group 3-5 years above the official age of entry into the last grade for a given level of education was selected for the calculation of the completion rate to allow for some delayed entry or repetition. In countries where entry can occur very late or where repetition is common, some children or adolescents in the age group examined may still attend school and the eventual rate of completion may therefore be underestimated.

The indicator is calculated from household survey data and is subject to time lag in the availability of data. When multiple surveys are available, they may provide conflicting information due to the possible presence of sampling and non-sampling errors in survey data. The Technical Cooperation Group on the Indicators for SDG 4 – Education 2030 (TCG) has requested a refinement of the methodology to model completion rate estimates, following an approach similar to that used for the estimation of child mortality rates. The model would ensure that common challenges with household survey data, such as timeliness and sampling or non-sampling errors are addressed to provide up-to-date and more robust data.

TARGET 4.2 By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary education

Proportion of children aged 24-59 months of age who are developmentally on track in health, learning and psychosocial well-being, by sex.

Early childhood development (ECD) sets the stage for life-long thriving. Investing in ECD is one of the most critical and cost-effective investments a country can make to improve adult health, education and productivity in order to build human capital and promote sustainable development. ECD is equity from the start and provides a good indication of national development. Efforts to improve ECD can bring about human, social and economic improvements for both individuals and societies.

The recommended measure for SDG 4.2.1 is the Early Childhood Development Index 2030 (ECDI2030) which is a 20-item instrument to measure developmental outcomes among children aged 24 to 59 months in population-based surveys. The indicator derived from the ECDI2030 is the proportion of children aged 24 to 59 months who have achieved the minimum number of milestones expected for their age group, defined as follows:

– Children age 24 to 29 months are classified as developmentally on-track if they have achieved at least 7 milestones – Children age 30 to 35 months are classified as developmentally on-track if they have achieved at least 9 milestones – Children age 36 to 41 months are classified as developmentally on-track if they have achieved at least 11 milestones – Children age 42 to 47 months are classified as developmentally on-track if they have achieved at least 13 milestones – Children age 48 to 59 months are classified as developmentally on-track if they have achieved at least 15 milestones

SDG indicator 4.2.1 is intended to capture the multidimensional and holistic nature of early childhood development. For this reason, the indicator is not intended to be disaggregated by domains since development in all areas (health, learning and psychosocial wellbeing) are interconnected and overlapping, particularly among young children. The indicator is intended to produce a single summary score to indicate the proportion of children considered to be developmentally on track.

The domains included in the indicator for SDG indicator 4.2.1 include the following concepts:

Health: gross motor development, fine motor development and self-care Learning: expressive language, literacy, numeracy, pre-writing, and executive functioning Psychosocial well-being: emotional skills, social skills, internalizing behavior, and externalizing behavior

The number of children aged 24 to 59 months who are developmentally on track in health, learning and psychosocial well-being divided by the total number of children aged 24 to 59 months in the population multiplied by 100.

SDG 4.2.1 was initially classified as Tier 3 and was upgraded to Tier 2 in 2019; additionally, changes to the indicator were made during the 2020 comprehensive review. In light of this and given that the ECDI2030 was officially released in March 2020, it will take some time for country uptake and implementation of the new measure and for data to become available from a sufficiently large enough number of countries. Therefore, in the meantime, a proxy indicator (children aged 36-59 months who are developmentally ontrack in at least three of the following four domains: literacy-numeracy, physical, social-emotional and learning) will be used to report on 4.2.1, when relevant. This proxy indicator has been used for global SDG reporting since 2015 but is not fully aligned with the definition and age group covered by the SDG indicator formulation. When the proxy indicator is used for SDG reporting on 4.2.1 for a country, it will be footnoted as such in the global SDG database.

Click on the button below to explore the data behind this indicator’s proxy; Children aged 36-59 months who are developmentally ontrack in at least three of the following four domains: literacy-numeracy, physical, social-emotional and learning . For more information about this proxy indicator, please see “Comments and Limitations”

Adjusted net attendance rate, one year before the official primary entry age

The indicator measures children’s exposure to organized learning activities in the year prior to the official age to start of primary school as a representation of access to quality early childhood care and pre-primary education. One year prior to the start of primary school is selected for international comparison. A high value of the indicator shows a high degree of participation in organized learning immediately before the official entrance age to primary education.

The participation rate in organized learning (one year before the official primary entry age), by sex as defined as the percentage of children in the given age range who participate in one or more organized learning programme, including programmes which offer a combination of education and care. Participation in early childhood and in primary education are both included. The age range will vary by country depending on the official age for entry to primary education.

An organized learning programme is one which consists of a coherent set or sequence of educational activities designed with the intention of achieving pre-determined learning outcomes or the accomplishment of a specific set of educational tasks. Early childhood and primary education programmes are examples of organized learning programmes.

Early childhood and primary education are defined in the 2011 revision of the International Standard Classification of Education (ISCED 2011). Early childhood education is typically designed with a holistic approach to support children’s early cognitive, physical, social and emotional development and to introduce young children to organized instruction outside the family context. Primary education offers learning and educational activities designed to provide students with fundamental skills in reading, writing and mathematics and establish a solid foundation for learning and understanding core areas of knowledge and personal development. It focuses on learning at a basic level of complexity with little, if any, specialisation.

The official primary entry age is the age at which children are obliged to start primary education according to national legislation or policies. Where more than one age is specified, for example, in different parts of a country, the most common official entry age (i.e. the age at which most children in the country are expected to start primary) is used for the calculation of this indicator at the global level.

The number of children in the relevant age group who participate in an organized learning programme is expressed as a percentage of the total population in the same age range. From household surveys, both enrolments and population are collected at the same time.

Participation in learning programmes in the early years is not full time for many children, meaning that exposure to learning environments outside of the home will vary in intensity. The indicator measures the percentage of children who are exposed to organized learning but not the intensity of the programme, which limits the ability to draw conclusions on the extent to which this target is being achieved. More work is needed to ensure that the definition of learning programmes is consistent across various surveys and defined in a manner that is easily understood by survey respondents, ideally with complementary information collected on the amount of time children spend in learning programmes.

TARGET 4.a Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for all

Proportion of schools offering basic services, by type of service.

This indicator measures the presence of basic services and facilities in school that are necessary to ensure a safe and effective learning environment for all students. A high value indicates that schools have good access to the relevant services and facilities. Ideally each school should have access to all these services and facilities.

The percentage of schools by level of education (primary education) with access to the given facility or service

Electricity: Regularly and readily available sources of power (e.g. grid/mains connection, wind, water, solar and fuel-powered generator, etc.) that enable the adequate and sustainable use of ICT infrastructure for educational purposes.

Internet for pedagogical purposes: Internet that is available for enhancing teaching and learning and is accessible by pupils. Internet is defined as a worldwide interconnected computer network, which provides pupils access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files, irrespective of the device used (i.e. not assumed to be only via a computer) and thus can also be accessed by mobile telephone, tablet, PDA, games machine, digital TV etc.). Access can be via a fixed narrowband, fixed broadband, or via mobile network.

Computers for pedagogical use: Use of computers to support course delivery or independent teaching and learning needs. This may include activities using computers or the Internet to meet information needs for research purposes; develop presentations; perform hands-on exercises and experiments; share information; and participate in online discussion forums for educational purposes. A computer is a programmable electronic device that can store, retrieve and process data, as well as share information in a highly-structured manner. It performs high-speed mathematical or logical operations according to a set of instructions or algorithms.

Computers include the following types: -A desktop computer usually remains fixed in one place; normally the user is placed in front of it, behind the keyboard; – A laptop computer is small enough to carry and usually enables the same tasks as a desktop computer; it includes notebooks and netbooks but does not include tablets and similar handheld devices; and – A tablet (or similar handheld computer) is a computer that is integrated into a flat touch screen, operated by touching the screen rather than using a physical keyboard.

Adapted infrastructure is defined as any built environment related to education facilities that are accessible to all users, including those with different types of disability, to be able to gain access to use and exit from them. Accessibility includes ease of independent approach, entry, evacuation and/or use of a building and its services and facilities (such as water and sanitation), by all of the building’s potential users with an assurance of individual health, safety and welfare during the course of those activities.

Adapted materials include learning materials and assistive products that enable students and teachers with disabilities/functioning limitations to access learning and to participate fully in the school environment.

Accessible learning materials include textbooks, instructional materials, assessments and other materials that are available and provided in appropriate formats such as audio, braille, sign language and simplified formats that can be used by students and teachers with disabilities/functioning limitations.

Basic drinking water is defined as a functional drinking water source (MDG ‘improved’ categories) on or near the premises and water points accessible to all users during school hours.

Basic sanitation facilities are defined as functional sanitation facilities (MDG ‘improved’ categories) separated for males and females on or near the premises.

Basic handwashing facilities are defined as functional handwashing facilities, with soap and water available to all girls and boys.

The number of schools in a given level of education with access to the relevant facilities is expressed as a percentage of all schools at that level of education.

The indicator measures the existence in schools of the given service or facility but not its quality or operational state.

For every child to learn, UNICEF has eight key asks of governments:

• A demonstration of how the SDG 4 global ambitions are being nationalized into plans, policies, budgets, data collection efforts and reports.
• A renewed commitment to education to recover learning losses and manage impacts of COVID-19.
• The implementation and scaling of digital learning solutions and innovations to reimagine education.
• Attention to skills development should be a core component to education.
• Focus to provide quality education to the most vulnerable – including girls, children affected by conflict and crisis, children with disabilities, refugees and displaced children.
• A continued commitment to improving access to pre-primary, primary and secondary education for all, including for children from minority groups and those with disabilities.
• A renewed focus on learning outcomes and their enablers, including learning in safe and adequate environments, support by well-trained teachers and structured content.
• The implementation of SDG-focused learning throughout schools to raise awareness and inspire positive action.

Learn more about  UNICEF’s key asks for implementing Goal 4

See more Sustainable Development Goals

ZERO HUNGER

GOOD HEALTH AND WELL-BEING

QUALITY EDUCATION

GENDER EQUALITY

CLEAN WATER AND SANITATION

AFFORDABLE AND CLEAN ENERGY

DECENT WORK AND ECONOMIC GROWTH

REDUCED INEQUALITIES

CLIMATE ACTION

PEACE, JUSTICE AND STRONG INSTITUTIONS

PARTNERSHIPS FOR THE GOALS

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Defining and measuring the quality of education

Strategic_seminar1.jpg.

What is the quality of education? What are the most important aspects of quality and how can they be measured?

These questions have been raised for a long time and are still widely debated. The current understanding of education quality has considerably benefitted from the conceptual work undertaken through national and international initiatives to assess learning achievement. These provide valuable feedback to policy-makers on the competencies mastered by pupils and youths, and the factors which explain these. But there is also a growing awareness of the importance of values and behaviours, although these are more difficult to measure.  

To address these concerns, IIEP organized (on 15 December 2011) a Strategic Debate on “Defining and measuring the quality of education: Is there an emerging consensus?” The topic was approached from the point of view of two cross-national surveys: the OECD Programme for International Student Assessment (PISA) and the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ)*.

Assessing the creativity of students

“Students’ capacity to extrapolate from what they know and apply this creatively in novel situations is more important than what the students know”, said Andreas Schleicher, Head of the Indicators and Analysis Division at the Directorate for Education, OECD, and in charge of PISA. This concept is reflected in current developments taking place in workplaces in many countries, which increasingly require non-routine interactive skills. When comparing the results obtained in different countries, PISA’s experience has shown that “education systems can creatively combine the equity and quality agenda in education”, Schleicher said. Contrary to conventional wisdom, countries can be both high-average performers in PISA while demonstrating low individual and institutional variance in students’ achievement. Finally, Schleicher emphasized that investment in education is not the only determining factor for quality, since good and consistent implementation of educational policy is also very important.

The importance of cross-national cooperation

When reviewing the experience of SACMEQ, Mioko Saito, Head a.i of the IIEP Equity, Access and Quality Unit (technically supporting the SACMEQ implementation in collaboration with SACMEQ Coordinating Centre), explained how the notion of educational quality has significantly evolved in the southern and eastern African region and became a priority over the past decades. Since 1995, SACMEQ has, on a regular basis, initiated cross-national assessments on the quality of education, and each member country has benefited considerably from this cooperation. It helped them embracing new assessment areas (such as HIV and AIDS knowledge) and units of analysis (teachers, as well as pupils) to produce evidence on what pupils and teachers know and master, said Saito. She concluded by stressing that SACMEQ also has a major capacity development mission and is concerned with having research results bear on policy decisions.  

The debate following the presentations focused on the crucial role of the media in stimulating public debate on the results of cross-national tests such as PISA and SACMEQ. It was also emphasized that more collaboration among the different cross-national mechanisms for the assessment of learner achievement would be beneficial. If more items were shared among the networks, more light could be shed on the international comparability of educational outcomes.

* PISA assesses the acquisition of key competencies for adult life of 15-year-olds in mathematics, reading, and science in OECD countries. SACMEQ focuses on achievements of Grade 6 pupils. Created in 1995, SACMEQ is a network of 15 southern and eastern African ministries of education: Botswana, Kenya, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania (Mainland), Tanzania (Zanzibar), Uganda, Zambia, and Zimbabwe

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Quality Matters

Barriers to high-quality ece, steps to improve quality ece, recommendations for pediatricians, recommendations for community-level actions, recommendations for national- and state-level actions, lead author, council on early childhood executive committee, 2015–2016, quality early education and child care from birth to kindergarten.

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

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Elaine A. Donoghue , COUNCIL ON EARLY CHILDHOOD , Dina Lieser , Beth DelConte , Elaine Donoghue , Marian Earls , Danette Glassy , Alan Mendelsohn , Terri McFadden , Seth Scholer , Jennifer Takagishi , Douglas Vanderbilt , P. Gail Williams; Quality Early Education and Child Care From Birth to Kindergarten. Pediatrics August 2017; 140 (2): e20171488. 10.1542/peds.2017-1488

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High-quality early education and child care for young children improves physical and cognitive outcomes for the children and can result in enhanced school readiness. Preschool education can be viewed as an investment (especially for at-risk children), and studies show a positive return on that investment. Barriers to high-quality early childhood education include inadequate funding and staff education as well as variable regulation and enforcement. Steps that have been taken to improve the quality of early education and child care include creating multidisciplinary, evidence-based child care practice standards; establishing state quality rating and improvement systems; improving federal and state regulations; providing child care health consultation; as well as initiating other innovative partnerships. Pediatricians have a role in promoting quality early education and child care for all children not only in the medical home but also at the community, state, and national levels.

Children’s early experiences are all educational, whether they are at home, with extended family and friends, or in early education and child care settings. Those educational experiences can be positive or negative. At present, more than half of children less than 5 years old regularly attend some type of out-of-home child care or early childhood program, 1 and their experiences in these settings will affect their future lives. 1 The arrangements families make for their children can vary dramatically, including care by parents and relatives, center-based child care, family child care provided in a caregiver’s home, care provided in a child’s own home by nannies or baby-sitters, or a combination of these types of care. 1 , – 3 How a family chooses this care is influenced by family values, affordability, and availability. 2 , 4 For many families, high-quality child care is not available or affordable. 2 , 4 This policy statement outlines the importance of quality child care and what pediatricians can do to help children get care in high-quality early childhood education (ECE) settings.

When care is consistent, developmentally appropriate, and emotionally supportive, and the environment is healthy and safe, there is a positive effect on children and their families. 5 , – 14 Children who are exposed to poor-quality environments (whether at home or outside the home) are more likely to have unmet socioemotional needs and be less prepared for school demands. 5 , – 14 Behavioral problems in ECE can lead to preschool expulsion with cascading negative consequences. Each year, 5000 children are expelled from ECE settings, which is a rate 3 times higher than that of their school-aged counterparts. 15 When behavioral health consultation is available to preschool teachers, the rate of reported expulsions is half that of the control population. 15 , 16  

Early education does not exist in a silo; learning begins at birth and occurs in all environments. Early brain and child development research unequivocally demonstrates that human development is powerfully affected by contextual surroundings and experiences. 17 , – 19 A child’s day-to-day experiences affect the structural and functional development of his or her brain, including his or her intelligence and personality. 17 , – 19 Children begin to learn to regulate their emotions, solve problems, express their feelings, and organize their experiences at an early age and then use those skills when they arrive at school. 19 The American Academy of Pediatrics (AAP) has recognized the importance of early brain and child development by making it a strategic priority.

Research of high-quality, intensive ECE programs for low-income children confirm lasting positive effects such as improved cognitive and social abilities (including better math and language skills than control groups). 5 , – 14 The indicators of high-quality ECE have been studied and are summarized in Table 1 .

Domains of Health and Safety Quality in ECE

Adapted from Stepping Stones 20  

There are different staff-to-child ratios for small-family homes, large-family homes, and centers. Ratios are also based on the ages of the children. Specific staff-to-child ratios are described in standard (1.1.1.2). 21  

Many families have no quality child care options in their immediate communities. 2 The positive effects from high-quality programs and the negative effects from poor-quality programs are magnified in children from disadvantaged situations or with special needs, and yet, these children are least likely to have access to quality early education and child care. 2 , 4 , 22 , 23 Barriers to high-quality ECE include inadequate funding and staff education as well as inconsistent regulation and enforcement. 15 Funding on the federal, state, and local levels (even when combined with parental fees) often does not provide adequate financial support to ensure proper training, reasonable compensation, or career advancement opportunities for the early education workforce. 2 , – 4 , 22 , – 25 Adequate compensation of early education providers promotes quality by recruiting and retaining trained staff and their directors. Young children, especially infants and toddlers, need stable, positive relationships with their caregivers to thrive, and staff retention helps maintain those strong relationships. 19 Budget restrictions also limit the number of children who can be served. 22 As of 2012, 23 states had wait lists for their child care subsidy programs, and many areas have wait lists for Head Start programs. 4 Finally, budget restrictions may limit a program’s ability to hire child care health consultants. ECE settings rarely have health professionals like school nurses despite the fact that the children served are younger, less able to express their symptoms, and are prone to more frequent infectious illnesses. 26 Some states require child care health consultants to visit infant and toddler programs regularly.

State regulations of ECE programs vary dramatically because of an absence of national regulation, and this contributes to variation in ECE quality. Family child care settings have different regulations than center-based care, and some forms of child care are exempt from regulation. 23 , 25 , 27 The variability in regulation, staff screening, staff training, and the availability of supports such as child care health consultation contribute to a wide variation in quality. Even when regulations are present, enforcement varies, and only 44 states conduct annual health and safety inspections. 23 , 25  

The definition of quality in ECE is becoming more evidence based as newer, validated measures become available. State licensing standards have been the traditional benchmarks, but they set a minimum standard that is typically considerably less than the recommendations of health and safety experts. 20 , 21 , 23 , 25 , 27 , 28 National organizations including the AAP, the American Public Health Association, and the National Association for the Education of Young Children have developed standards and voluntary systems of accreditation that are often more robust than state licensing regulations. The publication Caring for Our Children, Third Edition 21 includes evidence-based practice standards for nutrition, safety, hygiene, staff-to-child ratios, and numerous other subjects that have been shown to improve the quality of child care. 29 , 30  

The quality rating and improvement system (QRIS) is a method of quality improvement that is being implemented in >75% of states. 25 QRISs use research-based, measurable standards to define quality levels, which are often denoted by a star rating system. QRISs often use incentives (such as staff scholarships, tiered reimbursement for child care subsidies, and technical assistance and/or professional development) as strategies to improve ECE quality. Unfortunately, the QRIS does not always include key health and safety standards. Those who are responsible for implementing QRISs would benefit from input from pediatricians, who are familiar with health issues and with the challenges of translating research into practice. Child care resource and referral agencies are available nationwide, and they serve as regional resources for information about quality child care. They often also serve as a resource for QRIS implementation; however, most child care resource and referral agencies do not have adequate funding to hire early childhood health consultants as part of that technical assistance.

Improving access to child care health consultation is another way to positively affect the health and safety of children in ECE. Child care health consultants are health professionals who are trained to provide technical assistance and develop policies about health issues, such as medication administration, infection control, immunization, and injury prevention. 31 Child care health consultants also can provide developmental, hearing, oral health, and vision screenings and provide assistance with integrating children with special health care needs into ECE settings. 29 , 32 , 33  

The opportunities to use ECE programs to teach healthy habits (including healthy food choices, increased physical activity, and oral health practices) should not be overlooked. These messages can then be shared with families. Health screening services (such as vision and dental testing) also can be provided.

Innovative strategies to promote access to quality care and education also include state initiatives to promote cross-disciplinary teams (such as Early Childhood Advisory Councils), public-private funding partnerships, and universal preschool programs.

Ask families what child care arrangements they have made for their children, and educate them about the importance of high-quality child care. Resources include brochures (listed in Resources); checklists of quality, which can be accessed at www.aap.org/healthychildcare ; and referrals to local child care resources and referral agencies, which can be found at www.childcareaware.org .

Become educated about high-quality child care through the resources on the Healthy Child Care America Web site ( www.healthychildcare.org ), in Caring for Our Children , 21 and others (see Resources).

Be a medical home by participating in the 3-way collaboration with families and ECE professionals. The medical home concept of comprehensive, coordinated care is particularly critical for children with special health care needs. Three-way communication among the pediatricians, families, and ECEs can facilitate shared knowledge of the unique child care needs of children with special needs and foster implementation of child care policies and practices to meet those needs. 32 , 33 These activities are likely to improve access to ECE for these patients. Detailed care plans written in lay language assist in this collaboration. Medical team-based or time-based coding and billing may provide support for these efforts.

Advise families and early educators when children are having behavioral problems in ECE and are at risk for expulsion. Explain the triggers for behavior problems and recommend behavioral health resources as needed. 16 Some states have behavioral health resources available for young children through an Early Childhood Mental Health Consultation program. Read the AAP policy statement and technical report on toxic stress 19 and learn about the resources that are available through each state’s early care and education system.

Discuss the importance of guidelines on safe sleep, immunization, safe medication administration, infection control, healthy diet and physical activity, oral health, medical home access, and other health topics with local child care centers. Share resources such as Caring for Our Children , 21   Bright Futures , and the Healthy Child Care Web site ( www.healthychildcare.org ).

Become a child care health consultant or support your local child care health consultant nurses. Consider conducting a health and safety assessment in a local child care program by using a national health and safety checklist ( www.ucsfchildcarehealth.org ).

Educate policy makers about the science that supports the benefits of quality early child care and education and, conversely, the lost opportunities and setbacks that result from poor-quality care. 15 , 24  

Close the gaps between state regulations and the quality standards outlined in Caring for Our Children by encouraging strong state regulation and enforcement. Each AAP chapter has a legislative group that can help target these public policy makers with visits and letters. Nearly every AAP chapter also has an Early Childhood Champion, a pediatrician who is familiar with the early education and child care needs in that chapter and has knowledge about local resources to assist your efforts. Find your Early Childhood Champion at www.aap.org/coec .

Support a QRIS in your state if one is being implemented, and encourage robust child health and safety standards based on Caring for Our Children .

Advocate for improved funding for child care health consultation.

Encourage training of ECE professionals on health and safety topics, such as medication administration and safe sleep practices for infants. Consider providing training that uses the Healthy Futures curriculum provided on the Healthy Child Care Web site ( www.healthychildcare.org ).

Advocate and encourage expanded access to high-quality ECE through funding, such as expanded Child Care Developmental Block grants or Head Start funding. Reach out to legislators on the national and state levels to make the case for investing in quality early education as a good business, education, and social investment that has shown a strong return on investment. Encourage pediatric representation on state Early Childhood Advisory Councils or similar state groups to make the case to state officials personally.

American Academy of Pediatrics. Choosing Child Care: What’s Best for Your Family [Pamphlet]. Elk Grove Village, IL: American Academy of Pediatrics; 2002. Available through the AAP publications department: 800/433-9016 or at www.aap.org

American Academy of Pediatrics. The Pediatrician’s Role in Promoting Health and Safety in Child Care. Elk Grove Village, IL: American Academy of Pediatrics; 2001. Available at: www.healthychildcare.org

Child Care Aware, National Association of Child Care Resource and Referral Agencies (NACCRRA). Is this the right place for my child? 38 research-based indicators of quality child care. Available at: http://childcareaware.org/resources/printable-materials/

Child Care Aware, National Association of Child Care Resource and Referral Agencies (NACCRRA). Quality child care matters for infants and toddlers. Available at: http://childcareaware.org/families/choosing-quality-child-care

Child Care Resource and Referral Agencies, local referral agencies that can assist families in finding quality, affordable programs. Available at: http://childcareaware.org/families/choosing-quality-child-care/selecting-a-child-care-program/

Head Start. Early childhood learning and knowledge center. Available at: http://eclkc.ohs.acf.hhs.gov/hslc/tta-system/health

Healthy Child Care America. Federally funded and housed at the AAP, this Web site has many resources for health and ECE professionals. Available at: www.healthychildcare.org

National Association for the Education of Young Children. Developmentally Appropriate Practice in Early Childhood Programs Serving Children from Birth through Age 8. 3rd ed. Washington, DC: National Association for the Education of Young Children (NAEYC); 2009. Available at: www.naeyc.org/files/naeyc/file/positions/PSDAP.pdf

National Resource Center for Health and Safety in Child Care and Early Education. Available at: www.nrckids.org

Zero to Three. Early Experiences Matter Policy Guide. Washington, DC: Zero to Three; 2009. Available at: https://www.zerotothree.org/resources/119-early-experiences-matter-policy-guide

Zero to Three. Matching Your Infant’s and Toddler’s Style to the Right Child Care Setting. Washington, DC: Zero to Three; 2001. Available at: https://www.zerotothree.org/resources/86-matching-your-infant-s-or-toddler-s-style-to-the-right-child-care-setting

American Academy of Pediatrics

early childhood education

quality rating and improvement system

Dr Donoghue updated the previous policy statement and revised that original document by adding references, updating the wording, and adding new sections based on updates from the field. The document went through several layers of review, and Dr Donoghue was responsible for responding to those comments.

This document is copyrighted and is property of the American Academy of Pediatrics and its Board of Directors. All authors have filed conflict of interest statements with the American Academy of Pediatrics. Any conflicts have been resolved through a process approved by the Board of Directors. The American Academy of Pediatrics has neither solicited nor accepted any commercial involvement in the development of the content of this publication.

Policy statements from the American Academy of Pediatrics benefit from expertise and resources of liaisons and internal (AAP) and external reviewers. However, policy statements from the American Academy of Pediatrics may not reflect the views of the liaisons or the organizations or government agencies that they represent.

The guidance in this statement does not indicate an exclusive course of treatment or serve as a standard of medical care. Variations, taking into account individual circumstances, may be appropriate.

All policy statements from the American Academy of Pediatrics automatically expire 5 years after publication unless reaffirmed, revised, or retired at or before that time.

FUNDING: No external funding.

Elaine A. Donoghue, MD, FAAP

Jill Sells, MD, FAAP, Chairperson

Beth DelConte, MD, FAAP

Elaine Donoghue, MD, FAAP

Marian Earls, MD, FAAP

Danette Glassy, MD, FAAP

Alan Mendelsohn, MD, FAAP

Terri McFadden, MD, FAAP

Seth Scholer, MD, FAAP

Jennifer Takagishi, MD, FAAP

Douglas Vanderbilt, MD, FAAP

P. Gail Williams, MD, FAAP

Claire Lerner, LCSW, Zero to Three

Barbara U. Hamilton, MA, Maternal and Child Health Bureau

David Willis, MD, FAAP, Maternal and Child Health Bureau

Lynette Fraga, PhD, Child Care Aware

Abbey Alkon, RN, PNP, PhD, National Association of Pediatric Nurse Practitioners

Laurel Hoffmann, MD, AAP Section on Medical Students, Residents, and Fellows in Training

Charlotte O. Zia, MPH, CHES

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SDG-4 Quality Education

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• First Online: 12 July 2022

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• Sinan Küfeoğlu 2  

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

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Education is a component of sustainable development with its strong effects at global, regional and local levels. The biggest challenge the world faces in this context is the preservation and continuous improvement of the effort put forward to provide sustainable education in studies on education. The lack of chances for learning stymies social, economic and sustainable development and long-term stability and peace. This chapter presents the business models of 49 companies and use cases that employ emerging technologies and create value in SDG-4, Quality Education. We should highlight that one use case can be related to more than one SDG and it can make use of multiple emerging technologies.

The author would like to acknowledge the help and contributions of İlayda Zeynep Mert, Ömer Sami Temel, Abdullah Aykut Kılıç, Abdullah Enes Ögel, Veysel Ömer Yıldız and Enes Ürkmez in completing this chapter.

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• Sustainable development goals
• Business models
• Quality education
• Sustainability

Education is a component of sustainable development with its strong effects at global, regional and local levels. The biggest challenge the world faces in this context is the preservation and continuous improvement of the effort put forward to provide sustainable education in studies on education (Franco et al. 2020 ). The lack of chances for learning (also education) stymies social, economic and sustainable development and long-term stability and peace. Learning is especially essential for individuals who have been banned from formal schooling or who have not achieved basic skills and education. Learning is required to accomplish the 2030 Agenda for Sustainable Development, titled Transforming Our World (UN 2015 ), including 17 sustainable development goals (SDGs) and 169 related targets. The goal of providing opportunities for lifelong learning for everyone emphasises the global education agenda’s comprehensive character and its importance for achieving all SDGs by 2030. To provide comprehensive learning opportunities and systems, this integrated approach supports the concept that bridges must be built among and amongst actors, institutions, processes, learning places and times (Hanemann 2019 ).

The main purpose of the fourth SDG, under the title of “Quality Education”, put forward by the United Nations, is to encourage the principles and practices of sustainable development to create societies with exceptional opportunities in all fields of education (Franco et al. 2020 ). Today, almost 262 million children and adolescents are out of school. Sixty percent of school goers do not acquire basic numeracy and literacy skills in their first few school years. Seven hundred fifty million adults in the world are illiterate, which adversely affects the welfare of societies and reveals marginalisation (UNESCO 2021 ). Despite this situation, the enrolment rate in regions that continue to develop rose to 91% in 2015 because of active work carried out since 2000. As a result of these efforts, the number of children who are out of school has nearly halved. In addition, the significant increase in girls’ school enrolment and literacy rates are also among the remarkable achievements (United Nations Development Programme 2021 ). At the micro-level of society, the impact of epidemics of acute infectious disease on people, families and communities may be enormous. Children may lose their chance of going to school due to consequences or demands at home in the event of a large epidemic, at least until they are older (Kekić and Miladinovic 2013 ). These adverse outcomes of epidemics can easily be caused by pandemics as well. For example, the COVID-19 outbreak caused widespread school cancellations in 188 countries, affecting almost 1.5 billion children and adolescents. Only 30% of low-income countries have built a national distance learning platform. Nevertheless, over 65% of countries have done so. Almost 33% of young people in the world were already digitally excluded before the crisis. Also, girls have less access to digital technology than boys, restricting their online learning opportunities. It is especially challenging to reach children with mental or physical disabilities through online education programs. Distance education quality and accessibility will vary considerably within and between nations. Only 15 countries worldwide offer distance education in several languages (United Nations 2020 ). As a result, education plays a massive role in bringing societies to a certain level of resilience. All kinds of education are essential in generating sustainable development and in environmental problems, employment problems and industrial operation (UN Environment Programme 2021 ).

The objectives of SDG-4 concerning the problems as mentioned above are to present equality of opportunity based on literacy, numeracy and broader learning competencies, which are the most basic learning levels of education in general, from kindergarten, or nursery, to vocational schools and university (Unterhalter 2019 ). Expanding possibilities throughout all levels of education (preschool, primary, secondary, vocational, higher and adult education) is one of SDG-4’s goals. The goals expand the definition of education as a worldwide enterprise to include objectives in reading, numeracy and other areas such as global citizenship, sustainability and gender equality (Unterhalter 2019 ). Figure 6.1 demonstrates the targets set by the UN in the field of quality education under the name SDG-4 (United Nations 2021a , b , c ).

Targets of SDG-4 quality education (United Nations 2021a , b , c )

“Quality education”, one of 17 different development goals, emphasises an egalitarian, inclusive, quality and lifelong education content. Achieving the goals set in the scope of SDG-4 is also of great importance in terms of achieving other sustainable development goals. Along with literacy and access to primary education, higher educational institutions are considered to be highly influential in achieving sustainable development, with a social responsibility to bring forth a setting that cultivates sustainable development amidst their students and communities (Ferguson and Roofe 2020 ). In addition to SDG targets, trade activities in countries are directly related to education. The lack of educational opportunities in a particular region, that is, the lack of professional and personal skills of the people living in that region, has a significant impact on the creation of new business areas in the region and the disruption of various entrepreneurial and investment activities. Investing in people is of great importance for faster economic developments (Cervelló-Royo et al., 2020 ).

Although primary school attendance in developing nations has reached 91%, 57 million children are excluded from school. Many of the other SDGs can only be achieved through a good education. If people can get a good education, they can break the cycle of poverty (United Nations 2021a , b , c ; Patel20 2019 ). Due to high poverty levels, armed conflict and other emergencies, progress has also been hampered in developing regions. The number of youngsters out of school has risen due to the continuous violent situations in West Asia and North Africa. Although Sub-Saharan Africa has accomplished the most improvement of any developing region regarding primary school enrolment, substantial inequities still exist. Children from the poorest homes are four times more likely to drop out of school than those from the wealthiest households. Inequalities between rural and urban areas continue to be significant (Joint Sdg Fund 2021 ). Education has a critical role in reducing inequity and achieving gender equality. It also allows people worldwide to lead healthier and more sustainable lives (United Nations 2021a , b , c ).

Education also plays a role in fostering intercultural tolerance, promoting a more peaceful society (United Nations 2021a , b , c ). It is also a potent instrument for enhancing societal resilience. Formal and informal education and public awareness and training are essential for encouraging sustainable development, strengthening people’s and countries’ capacity to handle environmental and development concerns and establishing green and decent employment and industries (UNEP 2021 ). Education plays a significant part in developing tolerance in people interactions and the development of much more friendly communities. Fair access for females to education, medical care, decent jobs and involvement in economic and political institutions would improve humanity and the world economy’s sustainability. Funding in educational initiatives for females and raising the age of marriage would provide a fivefold return on investment (Koßmann 2019 ).

SDG-4 aims for all boys and girls to have equal access to elementary and secondary education and early childhood development programs and accessible university education for both men and women by 2030. This goal’s main aim is to increase young people’s numeracy and literacy abilities while also ensuring that all people, regardless of gender or handicap, have an equal chance (Joint SDG Fund 2021 ). Simultaneously, increased access to university education, as well as vocational and technical training, is emphasised. Within this context, available scholarships for students from developing nations to enrol in higher education, vocational training programs and other science programs in developed or developing countries are gradually increasing (Patel20 2019 ).

SDG-4’s main goal, which is to ensure that everyone, regardless of their race, gender, age or other characteristics, has access to inclusive and equal quality education, is ambitious and challenging to achieve. The way knowledge is passed down is presumed to change dramatically due to technological advancements, with a big move towards online platforms.

As an alternative to conventional methods of education, online education can be used to address specific challenges of SDG-4. Projections are ambiguous regarding the mix of online materials available to students in the future. Existing patterns indicate that a lot more online educational information is accessible, but it appears that considerably less of it would be used successfully by students. The ratio of rationales and ideologies between public and private content will continue to shift, but it appears that a few international content producers will start to control the industry (Unwin et al. 2017 ). A growing body of research aims to understand and explain the aspect of gender in online learning (Latchem 2014 ). Some suggest that online education methods are non-sexist and more gender-inclusive (Margolis and Fisher 2002 ). In contrast, others report that it does not solve pre-existing problems of traditional methods (Anderson 2004 ). Nevertheless, there is consensus that online platforms may offer more accessible knowledge, free exchange of information, networks and learning communities without regard to gender (Latchem 2014 ). Despite offering promising solutions, online education systems are not perfect. Literature suggests that, in developing regions and countries, women face the same challenges regardless of the educational platform, e.g. online vs traditional (Glen and Cédric 2003 ). It has been suggested that providing women with training and support in creating content that is appropriate to their needs and addresses their particular viewpoints, experiences and concerns would greatly help prevent their absence in online educational platforms (Latchem 2014 ).

Furthermore, virtual reality (VR) and different methods may also be used in the classroom. This would permit students to learn how to negotiate difficulties and communicate ideas online using new platforms. Forecasts regarding the future of education and the use of VR suggest that as gaming technologies are being created for the classrooms, augmented reality (AR) and VR are likewise expected to become much more common (Unwin et al. 2017 ). Campuses, as we know them today, may cease to exist. This would free learning from the confines of a physical school. A new campus would likely consist of mobile classrooms and a real-world setting. On the other hand, city libraries and laboratories would coexist to assist students in completing their assignments. Games that teach youngsters how to code, toys that teach robotics and various apps that help teachers quickly deliver knowledge to children are highly likely to become commonplace. The use of technology in education is expected to increase exponentially, aiding teaching and learning processes, thus evolving learning into being more creative and practical as time goes on. Conventional methods of performance and learning evaluations, such as tests, will likely be replaced by evaluations of students’ critical thinking and problem-solving abilities through their performance in creative projects (Nerdy Mates 2021 ).

Forecasts indicate that by 2025, the use of information and communication technologies (ICTs) in schools will be substantially more diversified. This makes predicting how it will be utilised in any given situation exceedingly challenging. Similarly, there will be some imaginative and unique situations in exceedingly disadvantaged contexts, where well-trained, incredibly inspiring educators will use ICTs to encourage kids to critically discover a wealth of information and thoughts, allowing them to build the abilities and understanding required to change the world in which they live. Furthermore, many governments’ educational systems will probably change. Many of these systems will expressly urge wider use of ICT in schools, driven in part by the interests of big multinational businesses and by a growing understanding of the impact advantages such technologies may provide. Educators will continue to play a critical role in education systems that schools still control. In the finest systems, even so, their function will have shifted from that of knowledge suppliers to that of mentors, assisting youngsters in learning to navigate the universe of digital data. This is especially important when working with disadvantaged children who may lack the parental and community support needed to organise and socialise education (Unwin et al. 2017 ).

6.1 Companies and Use Cases

Table 6.1 presents the business models of 49 companies and use cases that employ emerging technologies and create value in SDG-4. We should highlight that one use case can be related to more than one SDG and it can make use of multiple emerging technologies. In the left column, we present the company name, the origin country, related SDGs and emerging technologies that are included. The companies and use cases are listed alphabetically. Footnote 1

For reference, you may click on the hyperlinks on the company names or follow the websites here (Accessed Online – 2.1.2022):

http://www.solarpak.net/ ; https://alchemyimmersive.com/ ; https://bluecanoelearning.com/ ; https://bridge-u.com/ ; https://campuslogic.com/ ; https://coachhub.io/en/ ; https://codecombat.com/ ; https://cubomania.io/ ; https://delphia.com/ ; https://edutekno.com.tr/ ; https://elevateu.ai/ ; https://elsaspeak.com/en/ ; https://en.duolingo.com/ ; https://eonreality.com/ ; https://gethownow.com/ ; https://inurture.co.in/ ; https://learnwithhomer.com ; https://locorobo.co/index.html ; https://odem.cloud/ ; https://photomath.com/en/ ; https://riiid.com/en/main ; https://roybirobot.com/ ; https://scanmarker.com/ ; https://shop.robolink.com/ ; https://tab.gladly.io/ ; https://tinalp.com/ ; https://wondertree.co/ ; https://www.12twenty.com/ ; https://www.applyboard.com/ ; https://www.arduino.cc/ ; https://www.aurum3d.com/ ; https://www.avidbots.com/ ; https://www.betterup.com/ ; https://www.brainscape.com/ ; https://www.brightbytes.net ; https://www.century.tech/ ; https://www.civitaslearning.com/ ; https://www.coursera.org/ ; https://www.disciplina.io/ ; https://www.grammarly.com/ ; https://www.immerse.online/ ; https://www.mereka.my/ ; https://www.odilo.us/ ; https://www.ossovr.com/ ; https://www.packback.co/ ; https://www.talespin.com/ ; https://www.transfrvr.com/ ; https://www.verizon.com/ ; https://ziotag.com/

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Küfeoğlu, S. (2022). SDG-4 Quality Education. In: Emerging Technologies . Sustainable Development Goals Series. Springer, Cham. https://doi.org/10.1007/978-3-031-07127-0_6

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Quality Education

Young Peruvian boy practices reading while sitting at a desk. Photo: Elizabeth Adelman

What are levers for inclusive and quality education for refugees?

Over one half of children globally who are out of school live in conflict settings. Yet quality education is an essential component for securing a future for refugee children. REACH identifies ways to strengthen and build refugees’ “unknowable futures.” This work is relevant not only for refugees but for other young people globally who face similar, even if less extreme, uncertainties in the face of rapid globalization and technological change.

Are Refugee Children Learning? Early Grade Literacy in a Refugee Camp in Kenya

by Benjamin Piper, Sarah Dryden-Peterson, Vidur Chopra, Celia Reddick, and Arbogast Oyanga

Research article

Are Refugee Children Learning? Early Grade Literacy in a Refugee Camp in Kenya by Benjamin Piper, Sarah Dryden-Peterson, Vidur Chopra, Celia Reddick, and Arbogast Oyanga (2020) in The Journal on Education in Emergencies .

In the first literacy census in a refugee camp, the authors assessed all the schools providing lower primary education to refugee children in Kakuma refugee camp in Kenya. The outcomes for these students were concerningly low, even lower than for those of disadvantaged children in the host community, Turkana County. Literacy outcomes differed among the refugee children, depending on their country of origin, the language of instruction used at the school in Kenya, the languages spoken at home, and the children’s self-professed expectation of a return to their country of origin.

Refugee Education: Backward Design to Enable Futures

by Sarah Dryden-Peterson

policy engagement

Refugee Education: Backward Design to Enable Futures by Sarah Dryden-Peterson (2019) in Education and Conflict Review.

This short paper explores the use of backward design as a way to conceptualize refugee education policy and practice. Drawing on examples of classroom and research experiences, it proposes a planning template aimed at enabling refugee education policy and practice to facilitate the futures that refugee young people imagine and aim to create. 

Tío Emilio’s Story: A Tale from Nicaragua

written by Hania Mariën

Tío Emilio’s Story written by Hania Mariën (2019). A curriculum for students ages 10-14 .

Tío Emilio’s Story is a narrative of one individual’s pursuit of education during a time of conflict in Nicaragua. It offers students an insight into life in Nicaragua from 1967-1990, as experienced by one child on Isla Ometepe and touches upon issues of poverty, armed violence, and leaving home, as well as that of resilience and persistence. At its core, however, it is about learning to be a child. Educators and students are encouraged to learn with and from Tío Emilio and critically engage with Nicaraguan history, the United States’ involvement in the country, and to think about how conflict and military regimes can impact education.

Additional Resources

Video | What would it take to ensure that all refugee young people have access to learning that enables them to feel a sense of belonging? Refugee REACH founder and director Sarah Dryden-Peterson joined Steve Paikin on TVO’s The Agenda to discuss her book “Right Where We Belong: How Refugee Teachers and Students Are Changing the Future of Education,” and to explore this question.

Video | Refugee REACH director Sarah Dryden-Peterson delivers a lecture titled Refugee Education: Power, Purposes, and Pedagogies Across Contexts, hosted by NYU’s Global TIES for Children.

Podcast | Celia Reddick and Sarah Dryden-Peterson discuss language of instruction in refugee education on the FreshEd podcast, hosted by Will Brehm.

Video | Refugee REACH director Sarah Dryden-Peterson and students Esther Elonga, Martha Franco, Orelia Jonathan, and Kristia Wantchekon discuss how experiences of uncertainty affect the research design process amid multiple pandemics of Covid-19 and racism.

Insight | Student leaders and educators in Refugee REACH director Sarah Dryden-Peterson's new module at HGSE, Education in Uncertainty, share how they were able to connect their studies to practice and respond to emerging needs of their local communities and build supports during Covid-19.

Interview | Mary Winters, an HGSE alumna and now Programme Specialist with the LEGO Foundation, shares what it’s been like to put her classroom learning into practice, how she uses research in her work, and what keeps her going.

Research | This article finds that peer-to-peer group chats expand transnational learning opportunities and possibilities for instructional innovations, community engagement, and conversations about gender equity in refugee education.

Research | This article examines the quality of education available to refugees in both urban and refugee camp settings in Kenya, with a particular focus on teacher pedagogy.

Report | This policy report explores the educational histories of young refugee children in first-asylum countries, and identifies elements of these that are relevant to post-resettlement education in the United States.

Children’s Book | This resource details the personal account of Abdul, an Afghani child whose schooling was interrupted by armed conflict, but who never gave up in his pursuit for education.

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Equality and Quality in Education. A Comparative Study of 19 Countries

Fabian t. pfeffer.

University of Michigan

This contribution assesses the performance of national education systems along two important dimensions: The degree to which they help individuals develop capabilities necessary for their successful social integration (educational quality) and the degree to which they confer equal opportunities for social advancement (educational equality). It advances a new conceptualization to measure quality and equality in education and then uses it to study the relationship between institutional differentiation and these outcomes. It relies on data on final educational credentials and literacy among adults that circumvent some of the under-appreciated conceptual challenges entailed in the widespread analysis of international student assessment data.

The analyses reveal a positive relationship between educational quality and equality and show that education systems with a lower degree of institutional differentiation not only provide more educational equality but are also marked by higher levels of educational quality. While the latter association is partly driven by other institutional and macro-structural factors, I demonstrate that the higher levels of educational equality in less differentiated education systems do not entail an often-assumed trade-off for lower quality.

Introduction

The education system in modern society is supposed to fulfill two largely uncontested functions: First, equip individuals with knowledge that allows them to take part in social, economic, and political life ( Durkheim 1922 ). Second, confer access to valuable credentials independent of individuals' socio-economic background, in other words, provide opportunities for social mobility ( Coleman 1968 ; Labaree 1997 ). If we accept these two functions as fundamental elements of modern education systems, we should judge their performance according to the quality of knowledge they produce and the degree to which they provide equality of educational opportunities (in the remainder simply referred to as quality and equality). Both of these dimensions of educational outcomes are central and long-standing concerns of public policy and social science ( Hallinan 1988 ; Blossfeld and Shavit 1993a ).

This contribution asks whether countries can achieve both educational equality and educational quality simultaneously or whether certain institutional features of education systems may entail a trade-off between these two aims. In particular, I focus on the role of institutional differentiation – that is, the nature and timing of assigning students to different tracks or secondary schools ( Hopper 1968 ; Allmendinger 1989 ) – as a potential joint determinant of equality and quality in education. For a fruitful sociological approach to these questions, I propose different conceptualizations and measures of educational outcomes than those used in a growing field of comparative research. Although many contributions in this field share the theoretical motivation laid out here, most of them revert to a specific type of readily available and increasingly popular data, namely international student assessments, often with very limited appreciation of the conceptual limitations and assumptions these data entail.

An Equality-Quality Tradeoff in Education?

A fundamental question in sociological research on education and a primary concern of educational policy-making is whether socio-economic equality in educational opportunities can be increased without lowering the quality of education. I label this the potential equality-quality tradeoff in education.

The tension between the aims of equality and quality is nowhere more crystallized than in the controversy about the effects of institutional differentiation. The U.S. literature on institutional differentiation – here, in the form of tracking and ability grouping – serves as a case in point ( Oakes 1985 ; Barr and Dreeben 1983 ; Hallinan 1994 ). Despite formidable empirical evidence on the negative effects of tracking on equality ( Gamoran 1987 ; Gamoran and Mare 1989 ), defendants of differentiation argue for its positive effects based on the following mechanism: The sorting of students into different groups is supposed to increase classroom homogeneity with respect to student ability and learning potential. More homogeneous classrooms should allow more targeted instruction, which in turn is assumed to benefit students at all ability levels ( Figlio and Page 2002 ; Duflo et al. 2011 ). In this view, the abolishment or reduction of differentiation is seen as jeopardizing overall educational quality. Another version of this perspective goes beyond a concern for overall quality and specifically cautions against the dangers of decreasing quality at the top by exposing the highest achieving students to classrooms or schools with low achieving students.

Institutional differentiation has long been understood as the most central feature of education systems ( Hopper 1968 ). The great international variation in the nature and extent of differentiation makes this institutional characteristic a prime candidate for explaining cross-national differences in educational outcomes ( Kerckhoff 1995 ; 2001 ).

Comparative Evidence: Shortcomings & Alternatives

Existing comparative research based on student assessment data.

For the longest time, reliable empirical estimates of international differences in educational outcomes and, more so, their explanation were largely elusive ( Breen and Jonsson 2005 ). Large-scale, coordinated surveys that assess student outcomes in many countries, such as the International Mathematics and Science Study (TIMSS) or the Program for International Student Assessment (PISA), set out to provide a wealth of new data to rectify this situation. Since then, a number of contributions have drawn on these data to assess the association between educational equality and quality and the role of institutional differentiation for both of these outcomes (for a review see Van de Werfhorst and Mijs 2010 ).

Based on both TIMSS and PISA data, researchers have documented no or no consistent association between educational equality and quality ( Woessmann 2008 ; Schütz et al. 2008 ; Hanushek and Woessmann 2006 ; Hermann and Horn 2011 ). Furthermore, research has repeatedly shown that systems with more intense and early differentiation are marked by higher levels of socio-economic inequality in student test scores, that is, lower equality ( Ammermüller 2005 ; Marks 2005 ; Marks et al. 2006 ; Hanushek and Woessmann 2006 ; Brunello and Checchi 2007 ; Horn 2009 ; Schütz et al. 2008 ; Woessmann 2009 ). In contrast, the relationship between institutional differentiation and average test scores is much weaker. Researchers have found either no association ( Hanushek and Woessmann 2006 ; Robert 2010 ) or a small positive association that is sensitive to different model specifications ( Horn 2009 ). In short, the current literature based on student achievement data suggests that institutional differentiation is detrimental for educational equality and largely inconsequential for educational quality – a conclusion in line with the observation of a non-existent tradeoff between educational equality and quality.

Limitations of international student assessment data

Existing comparative studies based on student assessments thus yield rather consistent results. But are their findings robust to a different conceptualization and measurement approach? The alternative approach proposed here relies on measures of final competencies and credentials among adults to address the central sociological questions at stake in a more direct way and to circumvent some of the central limitations of student assessment data in answering those questions.

Most international achievement tests have been designed for the explicit purpose of measuring broad student competencies rather than the mastery of specific curricular content. For instance, PISA aims to provide measures of students' ability to “interactively use language, symbols, and text [to] function well in society [my emphasis]” ( OECD 2005 ); clearly a measure sociologist should indeed interested in ( Kingston et al. 2003 ). However, measuring these competencies and their distribution at a selected age or grade has obvious drawbacks. A test taken at, say, age 15 or in eighth grade, provides but a snapshot of a longer developmental trajectory of student competencies ( Kerckhoff 1993 ). We may hope that these snapshot measures are reliable predictors of later student outcomes in terms of both competencies and credentials. But even if they were, they do not allow us to estimate the contribution of the education system and its institutional design towards the creation and distribution of final student competencies and credentials.

Figure 1 helps illustrate why. It depicts stylized trajectories of growth in four different education systems. In country A, students from higher socio-economic (SES) backgrounds have a steeper learning curve than those from lower socio-economic backgrounds. The learning curves of these two groups diverge at a faster pace once differentiation has taken place. This pattern of cumulative advantage may emerge if socio-economically advantaged students are more likely to enter higher tracks where their learning is accelerated. Of course, any different measurement point would yield a much different estimate of both socio-economic inequalities in achievement and average achievement (see also Brunello and Checchi 2007 : p.829). Yet, interpolating to later outcomes is impossible if the shape of learning curves differs across nations: In country B, disadvantaged students are able to eventually catch up to their higher SES peers. This pattern of “late blooming” may arise from better-targeted instruction in lower tracks, track mobility, or continuing education after completion of a particular track or school. Finally, in countries C and D student learning follows the same trajectory as in country A but the timing of differentiation differs. While the comparison of countries A and C may serve to identify the impact of differentiation thanks to the fortunate timing of the student assessment before and after the onset of differentiation, respectively, a comparison of countries C and D fails to provide this same analytic potential because, in both cases, differentiation occurs after the measurement point. In essence, analyses based on data collected at age 15 (as in many international student assessments) cannot partial out the impact of relatively late differentiation in secondary schools compared to fully comprehensive secondary school systems. Unfortunately, differentiation at age 16 is prevalent in many countries ( Brunello and Checchi 2007 : p.799).

In sum, when it comes to their ability to provide sociologically relevant measures of educational equality and quality and, in particular, their relationship to institutional features of education systems, international student assessment data may not only be subject to measurement error – as noisy predictors of later outcomes – but also suffer serious conceptual problems that lead me to test the robustness of the conclusions from existing research by drawing on an alternative approach.

An alternative approach

The alternative I propose here is simple. It assumes that the success of education systems can be judged based on the final educational status of its adult population. More specifically, I claim that a sociological analysis of educational quality and equality should rely on measures of relevant final competencies and credentials attained.

To assess educational quality , I propose to draw on post-schooling measures of capabilities that serve as the functional prerequisite for social integration, such as multi-dimensional measures of adult literacy. Measures taken after the completion of formal schooling circumvent the conceptual problems pointed out above: They reflect the final outcome of different learning trajectories and different routes through the education system. Of course, post-schooling capability measures entail their own conceptual challenges. In particular, they may appear sensitive to influences from outside of the education system, such as on-the-job learning opportunities or more general societal conditions. Literacy and other capabilities are indeed also accumulated outside of formal schooling – however, not only by adults but also by students enrolled in schools. We know that even the learning of curricular content occurs when schools are not in progress, such as the summer break, and that this out-of-school learning is tremendously consequential for students' learning trajectories ( Heyns 1978 ; Downey et al. 2004 ). In this sense, measures of student capabilities may be no less sensitive to societal influences outside of formal schooling than measures of adult literacy – and the need to control for these influences is thus equally important in the analysis of both.

For a sociological assessment of educational equality , I propose to draw on measures of final educational attainment – educational degrees – rather than student test scores since they directly capture the distribution of valuable credentials that enable social mobility. While student assessments may be predictive of ultimate educational attainment – the degree to which they are has, however, not been established – they certainly provide a less direct and more error-prone measure of mobility-relevant educational outcomes.

Institutional & Macrostructural Contexts of Equality and Quality

How may these alternative measures of educational equality and quality relate to the structure of national education systems? In the theoretical hypotheses below, I focus on the central role of institutional differentiation but also consider possible associations with other institutional attributes and macro-social conditions that may account for bias in the effects of institutional differentiation.

Institutional differentiation

In comparative research, institutional differentiation is typically defined as the way in which educational opportunities are differentiated between and within educational levels through formal tracking or streaming as well as the timing and rigidity of student selection on the secondary level ( Allmendinger 1989 ; Müller and Shavit 1998 ; Buchmann and Dalton 2002 ; Buchmann and Park 2009 ). Given the breadth of research on tracking in the United States, it may be important to remind the reader that the degree of formal differentiation of the U.S. education system is considerably lower than that of many other, particularly European nations ( Rubinson 1986 ). Highly differentiated education systems, that is, systems with strong, stable, or early student selection into separate educational pathways increase the information requirements for students to successfully navigate their educational careers. Guidance and management skills of high status parents become more consequential in this environment and confer children from high status backgrounds additional advantage in selecting the right educational track ( Baker and Stevenson 1986 ; Pfeffer 2008 ). Once on this track, this advantage accumulates further in the form of steeper learning curves thanks to the exposure to more advanced curricular content (see DiPrete and Eirich 2006 : p. 286), making later changes of tracks more difficult. These are possible reasons for a negative relationship between the degree of institutional differentiation and educational equality.

In contrast, the relationship between institutional differentiation and educational quality is more difficult to ascertain a priori. As discussed above, defendants of institutional differentiation certainly offer a clear line of reasoning why this relationship should be positive: Higher average quality in highly stratified systems, so the argument goes, could result from both maximized achievement among high ability students whose learning progress is not hampered by the integration of lower ability students as well as higher achievement of lower ability students who profit from instruction geared to their needs. This argument assumes that the allocation of students to different classrooms is indeed based on ability – and its strength is thus limited by the extent to which factors other than ability impact the assignment process ( Brunello et al. 2007 ), such as socio-economic factors. Also, one may alternatively assume that the achievement of lower ability students decreases when they are grouped with other low ability students. A high degree of institutional differentiation has been shown to be associated with a “cooling out” of educational expectations among low achieving students ( Buchmann and Dalton 2002 ; Buchmann and Park 2009 ) 1 . While the average effect of institutional differentiation on quality is therefore difficult to hypothesize a priori, it is clear that the analysis of educational quality will greatly profit from a consideration of the full distribution of capabilities rather than just the average.

Other institutional characteristics of education systems

Of course, national education systems differ in many more ways than merely their degree of institutional differentiation. In this paper, I therefore consider additional institutional features that have been proposed as important determinants of either educational quality or equality.

First, the size of the post-secondary education sector. Clearly, we should expect higher educational quality in a country in which a larger share of the population participates in higher education. Regarding educational equality , it has been proposed that the size of the post-secondary sector positively correlates with educational equality since in countries with higher post-secondary participation rates the access to secondary education is more likely to be saturated, in which case inequalities at that level can be expected to decrease ( Raftery and Hout 1993 ). The size of the post-secondary sector is also a prime example of an institutional characteristic that intersects with institutional differentiation: Highly differentiated systems often limit access to higher education by tying it to the successful completion of a particular secondary track or school type. Assessing the influence of these two characteristics jointly will be particularly useful to shed further light on the role of institutional differentiation.

Second, the degree to which education meets the same standards nationwide, typically referred to as the degree of standardization ( Allmendinger 1989 ). Institutional standardization encompasses not only the distribution of educational outputs measured through national benchmarks or testing systems – a hotly debated field of educational policy – but also that of educational inputs, such as schools' economic resources or curricular contents. While this multifaceted nature of standardization defies a prediction of the relationship between standardization and educational quality , I hypothesize a positive relationship with educational equality . By definition, standardized systems show fewer local disparities in terms of content and quality of education ( Stevenson and Baker 1991 ). They may therefore reduce the potential impact of economic characteristics and information advantages of high status parents on the selection of higher quality schools.

Third, the degree of privatization of the education systems. Those believing in efficiency gains induced by market competition, such as proponents of school choice, hypothesize positive effects of privatization on educational quality . Since a simplistic market model ignores important market imperfections in the education sector (such as great levels of imperfect information among its “consumers” and significant transaction costs during the market-exit of “suppliers”, i.e. school closings), I expect to reject the hypothesis implied in this position. Regarding the relationship between privatization and educational equality , I refer to what Arum et al. (2007) have proposed as the dual character of private educational institutions: As “client-seekers”, private schools aim at increasing enrollment and exert efforts to include and support students that otherwise might encounter fewer opportunities in the public sector. However, as “status-seekers” they also compete for prestige with other private and public institutions and therefore may seek to exclude otherwise able students through forms of student selection that are open to socio-economic discrimination. These countervailing effects of status- and prestige-seeking of private institutions leave the relationship between privatization and educational equality to be established empirically.

Finally, I consider the possibility that cross-national differences in adult training and professional development may play an important role in increasing adult literacy, counterbalancing shortcomings and inequities of the formal education system.

Macro-structural contexts

Since educational processes do not occur in isolation from broader societal contexts, my analyses also consider other macro-social and economic factors that may account for the observed association between institutional differentiation and educational performance (see Marks 2005 ).

First, economists have produced extensive evidence on the relationship between education and economic development, arguing that educational investments are a central determinant of sustained economic growth ( Schultz 1961 ; Psacharopoulos 1992 ; Barro 1998 ; Hanushek and Woessmann 2008 ; but also see Ramirez et al. 2006 ). That is, highly developed countries attained their current wealth partly based on the successful production of educational quality . Of course, part of the positive relationship between educational quality and economic development may also arise from influences that flow the other way: Wealthy countries may provide living conditions that are conducive to human flourishing in general, and the further development of individuals' skills in particular. Rather than seeking to establish the causality or directionality of this relationship, I will consider whether my main hypothesis on the influence of institutional differentiation is biased by the varying level of economic development among the countries included in this analysis. The same applies to the relationship between economic development and educational equality . Forceful theoretical arguments in favor of the positive effect of economic development on educational equality have famously been made in the industrialism hypothesis. It states that “the more industrialized a society, the smaller the influence of parental status on educational attainment.” ( Treiman 1970 : p. 221). Although it has been rejected in most empirical research ( Hout and DiPrete 2006 ), it is fair to say that the industrialism hypothesis continues its existence as a widely held intuition and thus merits repeated empirical assessment.

Second, I consider a country's degree of a economic inequality and hypothesize a negative relationship to educational quality . Among industrialized countries, the labor markets of countries with higher levels of inequality tend to have a larger low-skill sector ( Scharpf and Schmidt 2000 ). As the name implies, this segment of the labor market requires less skills to begin with, but it may also confer less skills to workers than other sectors of the economy. In contrast, high-skilled white-collar occupations may serve to maintain or possibly even expand the literacy of workers by confronting them with a variety of texts, documents, and quantitative information on a daily basis. As a result, countries with a more extensive low-skill sector, that is, more unequal countries, should show lower levels of adult literacy. Regarding the relationship between a society's level of social inequality and educational equality scholars have argue that a significant reduction in social inequality in education can only be achieved by redistributing economic resources ( Jencks et al. 1972 ; Bowles and Gintis 1976 ) or, as a somewhat weaker version of this, that “long-term commitments to socioeconomic equality may lead to an equalization of educational opportunities” ( Blossfeld and Shavit 1993b : p. 19). Based on this perspective, one may expect a negative association between overall social inequality and educational equality.

Third, I follow other comparative research on mobility processes that controls for former socialist status of countries (e.g., Beller and Hout 2006 ; Hout 2007 ).

Data, Measures, and Methods

Data and sample.

This analysis draws on data from the International Adult Literacy Survey (IALS), an international comparative study assessing literacy in twenty industrialized nations. Although still much less frequently used than the international student assessment data reference above, these data have begun to support more comparative research over recent years ( Brunello and Checchi 2007 ; Pfeffer 2008 ; Van de Werfhorst 2011 ; Barone and van de Werfhorst 2011 ; Park and Kyei 2011 ; Gesthuizen et al. 2011 ). All countries participating in the IALS applied a common set of survey questions to a large, nationally representative sample of its adult population ranging in size between 1,500 and 6,000 individuals. For this analysis, I include the following countries, which collected data between 1994 and 1998: Belgium, Canada, Chile, the Czech Republic, Denmark, Finland, Great Britain (England and Wales), Germany (West-German respondents), Hungary, Ireland, Italy, Northern Ireland, Norway, New Zealand, Poland, Slovenia, Sweden, Switzerland (German-speaking part), and the United States. 2 Since the performance of education systems is the central focus of this contribution, individuals who have not attended school in the nation studied and instead obtained their highest educational degree in a foreign country, that is, most first generation immigrants, are excluded from this analysis. The analyses reported here are based on a sample of 25 to 65 year olds to also capture those who return to formal schooling at later points in their lives.

Stability analyses restricted to respondents aged 25-35, i.e. those for whom later-life influences on their literacy are reduced, yield the same substantive conclusion (available from the author). In further stability analyses I have replicated the presented analyses of educational equality based on a different dataset, the 1999 International Social Survey Program (ISSP), yielding the same substantive conclusions. 3

Measures of Educational Quality

The IALS applied a very comprehensive concept of functional literacy that captures the essence of what may be considered the functional preconditions for social integration. It defines literacy as the ability to “use printed and written material to function in society” and measures it in three distinct dimensions: Prose literacy refers to the ability to understand and use information from a variety of texts, such as newspaper articles or poems; document literacy refers to the ability to locate and use information contained in a variety of formal documents, such as medical prescriptions or job applications; and quantitative literacy refers to the ability to master everyday mathematical skills such as those involved in balancing a checkbook or calculating tip. Each of these dimensions is assessed based on numerous items, which are combined via Item Response Theory scaling into a continuous measure ranging from 0 to 500. The literacy measures are highly correlated across these three dimensions (r>.97 in the analytic sample). I average them to obtain a comprehensive measure of functional literacy. In addition to analyses of international differences in mean levels of literacy, I also investigate differences across the full distribution of literacy with a focus on the upper end (90th percentile) and the bottom (10th percentile) (see also Hermann and Horn 2011 ). 4

Measures of Educational Equality

The IALS collected information on respondents' as well as their parents' educational attainment. This information on educational degrees is provided in the original International Standard Classification of Educational Degrees (ISCED 1976), which intends to maximize the cross-national comparability of national educational degrees while maintaining within-country validity. One shortcoming of this scheme is its failure to adequately capture qualitative differences within educational levels. As a result, my measure of educational equality exclusively captures vertical inequalities and neglects important and multi-faceted forms of “horizontal differentiation” ( Gerber and Cheung 2008 ). While I discuss this shortcoming further in the conclusion, it is worth noting that there is evidence that the effects of institutional differentiation on educational equality are unbiased by certain aspects of horizontal differentiation, such as the distinction between vocational and academic tracks (see Pfeffer 2008 : 548-549).

I conceptualize educational equality as the degree to which individuals' final educational degree is independent of the educational status attained by their parents. A low association between the educational status of parents and their children indicates higher equality of educational opportunities. The strength of this association can be estimated in a loglinear framework. Pfeffer (2008) discusses the advantages of this method in detail and applies it to the same data to provide a parsimonious estimate of the overall degree of educational equality across all educational levels. Specifically, the uniform difference model ( Xie 1992 ; Erikson and Goldthorpe 1992 ) proves most effective in producing an estimate of cross-national differences in educational equality:

The central parameter of interest ( Φ k C ) estimates the degree of association between individuals' highest degree attained ( D ) and the highest degree attained by either of their parents ( O ) for each country ( C ) while constraining the pattern of intergenerational association in educational status to be constant across nations ( Ψ i j O D ) and controlling for cross-national differences in the aggregate distribution of educational degrees ( Φ i k O C , Φ j k D C ). After reverting the sign of these estimated “uniform difference” parameter estimates and centering them at the cross-national mean, higher values stand for higher levels of educational equality and zero indicates the cross-national average (for further details see Pfeffer 2008 : pp.549-553).

Measures of institutional and macrostructural contexts

For the assessment of institutional characteristics of education systems, that is, the independent variables of the comparative analysis, I draw on existing classifications of countries along the institutional dimensions outlined above (see Müller and Shavit 1998 ; Buchmann and Dalton 2002 ; Pfeffer 2008 ; Buchmann and Park 2009 ). The institutional feature at the center of this contribution, differentiation, is measured in three categories: Weak differentiation entails that most students attend comprehensive schools, that they are able to move from one track to another even if this does not necessarily occur very frequently, and that access to post-secondary education is not formally predetermined by the choice of one track. High differentiation, on the other hand, describes systems that divide students into separate schools of which only one or some types prepare for post-secondary education and others are ‘dead-end’ pathways that preclude the attainment of higher levels of education. Mobility between these schools is also very limited. Finally, I distinguish a separate type of highly differentiated systems where students are selected for different types of secondary schools at a very early age (typically grades four to six) and these decisions are basically irrevocable as mobility between school types is minimal. A further characteristic that these systems share is a strong vocational sector designed to lead students from lower-track secondary schools through an apprenticeship system into skilled occupations and in some cases, as an option of higher vocational education, to master craftsmanship ( Meister ). This is the model followed by the German and Swiss system of dual vocational education, which combines company-based training with formal school-based instruction ( Müller and Shavit 1998 ). Since this strong vocational orientation is in many ways intended to compensate for strong and early selection, I label these systems as marked by “early and compensated differentiation”. Whether this compensatory purpose is indeed accomplished awaits empirical investigation.

While I believe that this qualitative and coarse categorization appropriately captures the Gestalt of institutional differentiation, others may prefer a different approach that draws on more detailed quantitative indicators of differentiation, such as the share of students assigned to differentiated learning environments during primary and secondary schooling, the number of different school types, or the typical age at which differentiation begins. I use such indicators collected by other researchers ( Brunello and Checchi 2007 ; Horn 2009 ) for sensitivity analyses.

The measures of institutional standardization, the prevalence of private schooling, and the relative size of the post-secondary education sector are described in Appendix A.1 . I treat these institutional features as control variables to assess whether they drive part of the effects of institutional differentiation on quality and equality. Similarly, I introduce controls for other macro-structural features of the nations studied, namely the level of economic development and the extent of social inequality (also see Appendix A.1 ). Again, I stress that I neither seek to establish the causality or directionality of any of these effects nor should my analyses be construed as an exercise in capturing all determinants of educational quality and equality. Instead, I introduce these additional characteristics to reduce the potential bias resulting from the exclusive focus on school factors when assessing educational outcomes.

The institutional and macro-economic measures are chiefly based on information from around the time at which the IALS data were collected. However, the educational participation of the individuals included in my analyses spans four full decades. While many of the institutional characteristics studied are subject to a great degree of path dependence, which limits the over-time variability of cross-national differences in these characteristics, changes are certainly possible. Where available, I test the stability of my results by using institutional indicators based on different decades (see Appendix A.2 ) as well as a replication of the analyses based on the youngest cohort only, for which the timing of the institutional information is most appropriate, yielding the same substantive results (available upon request).

The equality-quality tradeoff

Figure 2 (for country labels see Appendix A.1 ) displays the relationship between a nation's degree of educational equality and educational quality. We observe a clear though not perfect positive relationship between these two outcomes (r = .30). This is good news: Rather than implying a trade-off between equality and quality, education systems can perform high on both dimensions, as exemplified for instance by Scandinavian countries like Sweden, Denmark, or Finland. However, we can also already identify some exceptions to this general trend. Germany, for instance, stands out as a country with a rather high level of educational quality in combination with comparatively low levels of equality. The same could be said for Belgium, Switzerland, and Norway.

Figures 3 and ​ and4 4 report the relationship between educational equality and quality at the top and the bottom of the distribution of literacy, respectively. In particular the assumption of a trade-off between equality and quality at the top cannot be confirmed empirically. If anything, educational equality is even more clearly positively related to quality at the top of the distribution than at the bottom.

The positive correlation between equality and quality documented here already foreshadows that important institutional factors may influence both dimensions in the same direction. Below, I determine whether institutional differentiation is one such institutional characteristic that drives both educational equality and quality.

Institutional differentiation and educational equality

My assessment of the relationship between institutional differentiation and educational equality draws on Pfeffer's (2008) analysis and extends it by considering additional macro-structural factors. 5 Model 1 in Table 1 reveals that highly differentiated education systems are marked by significantly less equality than education systems with a low degree of differentiation (reference category). Systems with early and compensated differentiation, of which there are two in this country sample, do even worse. 6 In models 2 through 5, other institutional characteristics are added as separate controls, following a common strategy in comparative research that is based on a limited number of nations and that consequently lacks the statistical power to introduce all or at least more controls at once. The important conclusion to draw from the latter models is that the relationship between institutional differentiation and educational equality is largely unaffected by other institutional characteristics of education systems, which themselves do not appear to exert independent influences on the level of equality. Models 6 through 9 assess whether the effects of institutional differentiation are also stable to the inclusion of other macrostructural characteristics that have been hypothesized to impact educational equality. The answer is yes. None of the macro-structural factors exerts any notable influence on educational equality and thus leaves the main effects of institutional differentiation substantively unaltered.

Note: OLS regression coefficients; s.e. in parantheses (

As argued above, an alternative approach to measuring the influence of institutional differentiation relies on more detailed, quantitative indicators of institutional contexts. Appendix A.2 reports the results based on such alternative specifications, which also reveal a strong negative association between differentiation and educational equality.

Institutional differentiation and overall educational quality

Applying the same sequence of regression models, Table 7 reports the findings for average educational quality Model 1 reveals a negative relationship between differentiation and educational quality when focusing on the difference between low and highly differentiated systems for the moment. The penalty of high institutional differentiation amounts to 37 points (more than half a standard deviation in the literacy score). As apparent in models 2, 3 and 5, this penalty is also observed when we take into account different levels of standardization, privatization, and adult training, respectively, which do not appear to influence educational quality. The relative size of the post-secondary sector (model 4), however, shows a clear and strong positive effect (on average, a 1.7 point increase in literacy for every one percent increase in the post-secondary graduation rate). I did hypothesize higher education to increase average literacy – any other finding would have been disheartening. Since institutional differentiation and the size of the post-secondary sector are positively related – by definition, highly differentiated countries close off access to post-secondary education for part of the student population – the latter also mediates part of the observed effect of institutional differentiation, which drops from 37 to 28 points. That is, the negative association between differentiation and educational quality is partly explained by the rationing of higher education that is a more common feature of highly differentiated systems.

The interpretation of the relationship between institutional differentiation and educational quality requires more nuance when taking into account the two countries with a system with high, but early and compensated, differentiation. Model 1 of Table 7 shows that the average level of educational quality in these systems does not differ significantly from that in countries with a low degree of differentiation. One interpretation of this finding – that I return to below – is that the compensatory function of a strong vocational sector successfully sustains the continued growth of literacy among those individuals selected into the lower tracks of highly differentiated systems. Once I control for the size of the post-secondary sector, there is even tentative evidence for a slight quality advantage of these systems compared to countries with low differentiation. That is, without the rationing of access to higher education implied in these systems, they may even yield small benefits in terms of average quality.

Continuing with models 6 through 9, both hypotheses regarding the influence of macrostructural characteristics on educational quality are confirmed empirically: Wealthier countries show significantly higher levels of educational quality, whether we conceptualize national wealth as per capita gross domestic production (model 6) or the level of industrialization (model 7). More unequal countries, on the other hand, show lower levels of educational quality (model 8). Importantly, both macrostrutural features also bias the effect of institutional differentiation since highly differentiated countries in this sample also tend to be less well-off and more unequal (see Appendix A.1 ). Considering either of these features decreases the gap between countries with high and low institutional differentiation by 30-40 percent. Finally, model 9 confirms the stability of the findings to the consideration of a nations' experience of socialism.

Again, the alternative specification of the effects of institutional differentiation as measured by quantitative indicators (see Appendix A.2 ) support the conclusions drawn from Table 7 .

Institutional differentiation and educational quality across the distribution

High and low quality.

The evidence just presented does not yet directly speak to the idea that institutional differentiation may help maximize capabilities at the top and/or hurt the development of skills at the bottom. For that, Table 3 provides a description of the distribution of literacy at the top, the mean, and the bottom across the three types of institutional differentiation. We observe that systems with compensatory differentiation and systems with low differentiation are similar not only in terms of average quality but also at the bottom and the top of the distribution. Countries with high differentiation but without a strong vocational sector perform comparatively poorly across the points of the distribution chosen here and particularly so at the bottom.

One of the reasons for this may be the lack of a strong vocational sector. Poland may serve as an illustrative case (and representative of several Eastern European countries that fall into this institutional category): Despite a general emphasis on technical education in Poland – reflected in an option of technical secondary education, called technicum, as well as a lower-level track with a terminal vocational degree -, its vocational sector does not offer a sustained vocational pathway that extends from school- and employer-based apprenticeships to continued professional education for master craftsmanship. The latter, found in Germany and Switzerland, may help overcome some of the quality-reducing effects of high differentiation ( Köllo 2006 ), in other words fulfill its presumed compensatory function. However, additional analyses (available from the author) also reveal that this is only a partial explanation for these countries' performance in terms educational quality. Among those who report their highest degree to be upper secondary schooling, individuals with a terminal vocational degree in Germany and Switzerland do indeed fair better compared to those holding such degree in other countries. Yet, the main difference in literacy outcomes between these two countries and the rest lies in academically oriented education: German and Swiss respondents with a terminal academically-oriented secondary degree ( Abitur and Matura, respectively) outperform their counterparts in most other countries, joining the Scandinavian countries at the top of the literacy ranking. While I have earlier shown that the overall level of adult literacy is reduced by the role of the academic track in rationing access to university studies ( Table 7 ), this track is still highly successful in producing educational quality for a selected part of the population. Resistance towards ongoing educational reform efforts in Germany that seek to diminish the degree of institutional differentiation is typically framed as a need to conserve this kind of quality benefit of the academic track.

The reported associations between differentiation and quality at the top and the bottom are impacted by other institutional and macro-economic factors ( Tables 6 and 2 in Appendix A.3 ) in much the same way as quality at the mean ( Table 7 ): The influence of institutional differentiation is reduced when controlling for the size of the post-secondary sector, the level of economic development, and the degree of social inequality, but not affected by the inclusion of other institutional indicators, such as institutional standardization, privatization, and (at the top) the incidence of adult training. In the multiple regressions we can again observe the more detrimental consequences of high differentiation for quality at the bottom compared to the top (a gap of 50 points in Table 2 , model 1 compared to 30 points in Table 6 , model 1).

Note: Linear regression coefficients; s.e. in parantheses (

The full distribution of educational quality

Lastly, to provide a yet more detailed look at the full shape of educational quality beyond two arbitrarily chosen percentiles, Figure 5a reports the distribution of individual-level literacy scores aggregated by type of institutional differentiation. The earlier finding of lower average literacy in nations with a highly differentiated education system is reflected in the left shift of the distribution of educational quality in these countries. Also, the bulge at the lower end of this distribution corresponds to the earlier finding of higher penalties of institutional differentiation at the bottom (10th percentile). For the two countries with early and compensated differentiation, the distribution of educational quality appears compressed mainly because this curve aggregates individual literacy values from only two countries and the smoother curves for the other types are based on aggregate information from more countries (nine with low differentiation and eight with high differentiation, respectively). If these curves are displayed for each country separately, the countries with very high institutional differentiation indeed do not stand out as countries with an exceptionally compressed quality distribution (available from the author).

From the regression analyses presented above we have learned that the association between institutional differentiation and educational quality is reduced when controlling for other institutional and macro-structural institutional factors. To adjust for these factors in this distributional analysis, I regress individual literacy scores on all contextual variables (excluding differentiation) based on a sample of individuals pooled across all countries. The difference between the expected and observed literacy scores (i.e., the regression's error term) provides a measure of quality that is purged of these other contextual effects. The distribution of this adjusted literacy score ( Figure 5b ) is more similar across types of institutional differentiation than in its raw version. In other words, controlling for all contextual characteristics at once (which is not feasible in the macro-level regression models) further reduces the quality differences between systems with different levels of differentiation. But even after these adjustments, we can still observe a left skew for highly differentiated systems caused by a persisting bulge at the bottom of the distribution – likely accounting for the overall negative effect of high differentiation found in the macro-level regressions ( Table 7 ).

Summary and Conclusion

This contribution assesses two of the most fundamental functions of national education systems, the creation of skills that enable individuals' integration into society and the provision of opportunities for social mobility. I have described and explained cross-national differences in the quality of educational outcomes and the degree of equality in educational opportunity by drawing on an approach that circumvents the considerable conceptual limitations entailed in widely used data from international student assessments. I have laid my explanatory focus on the arguably most central institutional characteristic of national education systems, the differentiation of learning opportunities at the secondary level, and additionally taken into account the role of other institutional characteristics and macro-structural factors.

The results presented here contain several positive messages. First and foremost, I could not detect any evidence for a trade-off between educational quality and equality (of opportunity). Quite the opposite is the case: Countries with better outcomes on one of these performance dimensions also tend to do better on the other dimension. Educational policy makers therefore do not have to choose between two valued outcomes when attempting to reform education systems.

One of the most influential features of national education systems, which is amenable to reform, is the nature and intensity of student selection into different educational tracks and school types. For this specific institutional characteristic, the presented analyses confirmed clear, consistent, negative effects on educational equality. The fact that this association is stable to the consideration of other institutional and macroeconomic features provides more confidence in the potential equality-enhancing effects of reforms that decrease institutional differentiation ( Meghir and Palme 2005 ; Pekkarinen et al. 2009 ). Opposition to this type of reforms has mainly been based on the concern that less differentiated education systems may produce poorer quality outcomes. The empirical evidence produced here does not lend support to this concern. Countries with a high degree of differentiation are in fact marked by lower levels of educational quality, although a large part of this disadvantage arises from other factors, such as a relatively small post-secondary education sector, lacking economic growth, and high levels of economic inequality. This contribution shows that it is important to consider such characteristics to adequately explain cross-national differences in educational quality. It should, however, also be noted that even with these controls in place I still observe a higher proportion of adults with very low literacy outcomes in highly differentiated systems.

A sweeping critique of institutional differentiation as the perpetrator of low levels of literacy, on the other hand, also seems unwarranted. The countries included in this analysis with the most highly differentiated education systems, namely Germany and Switzerland, do not suffer from comparatively lower quality at the bottom (nor, for that matter, do they show higher quality at the top). I have ascribed this finding to a strong vocational sector that partly compensates for the quality losses associated with differentiation as well as to the concentration of educational quality in the top-most academic tracks in these systems. Broad statements about the quality-enhancing or quality-restricting effects of institutional differentiation are also limited by the fact that other institutional characteristics and macro-structural features play an important role in accounting for differences in educational quality between systems of varying degrees of institutional differentiation. Nevertheless, a conservative interpretation of my findings nevertheless suggests that ambitious educational reforms aimed at increasing equality of educational opportunities by decreasing the differentiation of the education system could be able to do so at no cost in terms of educational quality. This conclusion may be particularly relevant for those countries in which institutional differentiation is highest, Germany and Switzerland, and resistance to reform still substantial. In addition, arguments in the defense of high differentiation that (in many cases wrongly) assume superior educational quality fail to appreciate the quality-reducing effect of the restricted access to post-secondary education that typically accompanies high institutional differentiation.

The aim of this study has been to find answers to fundamental sociological questions about the performance of education systems and I have argued that comparative analyses addressing these questions with international student assessment data are based on problematic conceptual grounds. Using a different conceptualization and measurement approach, this contribution nevertheless attests to the robustness of many of the conclusions in this prior research, such as the central role institutional differentiation in explaining levels of educational quality and equality. On the other hand, the approach applied here allows the analysis of factors that research based on student assessment data cannot consider, most importantly the role of the size of the post-secondary sector as a mediator of some of the observed associations between institutional differentiation and educational quality.

The approach used here may also invite future research that extend and tests its findings.

First, as pointed out earlier, my assessment of educational equality has only selectively attended to horizontal inequalities in education. That is, the analyses neglect important and multi-faceted forms of “horizontal differentiation”, such as quality and prestige differences between schools and colleges ( Gerber and Cheung 2008 ). A growing body of research focuses on these complex and sometimes more hidden forms of differentiating students within a given educational level. A cross-national comparative study attempting a joint assessment of the effects of horizontal differentiation on both education equality and quality would be particularly interesting. My own analyses of an arguably more fundamental form of educational differentiation should provide a convenient starting point. In addition, future research may seek to assess how the (vertical) educational equality assessed here relates to horizontal equalities. In fact, similar to existing theories on the development of educational equality over time ( Lucas 2001 ), one may hypothesize a trade-off between these two dimensions, with horizontal inequalities being more important in nations with low vertical inequality.

Second, the measures of educational degrees used here face another challenge. Educational credentials are subject to meaningful cross-national differences in terms of their influence on individuals' life chances ( Shavit and Müller 1998 ; Barone and van de Werfhorst 2011 ; Van de Werfhorst 2011 ). The question is whether these cross-national differences in educational returns challenge my conclusions about the negative relationship between differentiation and equality. Prior work has shown that in countries with higher institutional differentiation, educational degrees tend to be more closely tied to labor market outcomes ( Allmendinger 1989 ; Müller and Shavit 1998 ). Consequently, inequality in educational opportunities, which has been shown to be higher in these countries, translates more directly into inequality in life chances; while in countries with less institutional differentiation, the degree to which those at the lower ranks of the educational hierarchy are also relegated to the lower ranks of the labor market may be comparatively weaker. This suggests that I have presented conservative estimates of the negative relationship between differentiation and equality.

Third, one may be tempted to consider whether international student assessment could be improved to reduce some of the conceptual challenges I have pointed out. One theoretical solution would be to collect student assessments at the completion of final schooling. However, vast individual variation in the length of educational participation and, in particular, the possibility of individuals returning to formal education at later life stages make it exceedingly difficult to collect meaningful end-of-schooling measures in cross-sectional, large-scale comparative surveys. For instance, student surveys designed with this purpose in mind have collected assessment data in the last year of compulsory schooling (“Population 3” in TIMSS), failing to capture both those who dropped out of formal schooling as well as the learning growth of those participating in higher education and adult education ( Porter and Gamoran 2002 : p.10). Analyzing adults appears to be a much more feasible solution.

Fourth, I have based the analysis of educational quality on a measure of literacy that can be viewed as an important, but certainly not the only relevant capability for social integration. Future research and data collections may seek to assess different capabilities assumed relevant for integration into the “knowledge society”, such as the ability to weight the trustworthiness of different sources of information or the ability to engage in complex reasoning. Although the investigation of the distributional shape of the literacy measure used here did not lead to any concern about ceiling effects, future research may also direct more attention to different measures of educational quality at the very top of the distribution: If further economic growth of most industrialized nations chiefly relies on technical innovations and creativeness, the crucial quality at the top may be less the ability to skillfully perform math tasks and understand a variety of written materials but to engage in the creative solution of much more complex analytical problems.

Fifth, although this study addresses questions that form part of lively policy debates, those exclusively concerned about causal inference to identify promising policy interventions are bound to be disappointed. The strength of this contribution lies in addressing broad, structural issues about education based on a new conceptualization and measurement approach. Like most cross-national comparative research and much of macro-sociological research in general, it provides associational evidence. This associational evidence speaks to a central macro-level issue, relies on careful empirical modeling, and is line with theoretically motivated hypotheses – which is why it should not be outright discarded due to a lack of causal inference. Instead, I note the potential attractiveness of the proposed conceptualization for those interested in adding credibility to a causal claim about the effects of institutional characteristics of national educational systems. Since the analysis of macro-level data does not invite the same econometric approaches to causal inference that are available for individual-level data, identification needs to come from a plausibly exogenous policy change involving, for the topics studied, a radical institutional discontinuity. This type of changes happen, but very seldom ( Gamoran 1996 ; Meghir and Palme 2005 ). By using data from several cohorts of the adult population, this approach provides coverage of different historical periods (unlike age-standardized student assessments) and thus maximizes the opportunity for observing a fitting natural experiment.

Many of the suggested extensions will need to rely on other and new comparative data. Researchers may combine information from a variety of sources to separately estimate and explain the degree of equality and quality in education – instead of relying on a single data source containing information on both dimensions as I did here. The increasing availability of cross-nationally comparable measures of educational degrees and socio-economic background ( Hoffmeyer-Zlotnik 2003 ; Schneider 2008 ) greatly facilitates the assessment of socio-economic inequalities in educational attainment for a broad range of countries and time points (e.g., Blossfeld and Shavit 1993a ; Shavit et al. 2007 ; Hertz et al. 2007 ; Brunello and Checchi 2007 ; Breen et al. 2009 ). The choices are unfortunately still more limited for the assessment of educational quality among adults. While a follow-up to the IALS, the 2003 Adult Literacy and Lifeskills Survey (ALL), was implemented in only six countries, recently released data from the Programme for the International Assessment of Adult Competencies (PIAAC) contain adult skill measures for as many as 23 countries.

• I compare countries' levels of educational quality and equality of opportunity and study how they relate to selected institutional characteristics of education systems, in particular institutional differentiation
• I advance a new conceptualization to measure quality and equality based on final educational credentials and literacy among adults drawing on data from the International Adult Literacy survey
• I find no trade-off between educational quality and equality. In particular, I demonstrate that the higher levels of educational equality in less differentiated education systems do not entail an often-assumed trade-off for lower quality

Acknowledgments

I gratefully acknowledge helpful comments on this project from Anna Chmielewski, David Harding, Daniel Horn, John Meyer, Yossi Shavit, Herman van de Werfhorst, as well as three anonymous reviewers. An earlier version of this paper has been presented at the “Youth Inequalities” Conference at the University College Dublin. The data for this project have been made available by Statistics Canada. I gratefully acknowledge use of the services and facilities of the Population Studies Center at the University of Michigan, funded by NICHD Center Grant R24 HD041028.

A.1 Institutional and Macrostructural Features

Differentiation: Classification of countries according to the overall degree of differentiation based on narrative descriptions of national education systems (for a brief description of each system that illustrates the arguments behind the classificatory decisions for this and following institutional characteristics see Pfeffer 2008 ). The categories are described in detail on page 11, in particular the group of countries with a special form of high differentiation that also includes early selection and a strong vocational system (high a ).

Standardization: Classification of countries according to the overall degree of standardization of educational governance and contents. Classifying the British system as unstandardized can be debated (see also Müller and Shavit 1998 : 12). The same holds for Slovenia. Reclassifying these cases as standardized does not affect the reported conclusions.

Privatization: Published figures on the share of private enrollment are subject to a good deal of historical change (for instance, Chile implemented radical privatization policies in the 1980s). They are therefore not used as strict and precise measures but instead to supplement a broad categorization based on narrative accounts.

Post-secondary education: Given that the IALS data consist of nationally representative samples, a convenient way to measure the inclusiveness of the post-secondary sector is to compute the population share of post-secondary degree holders directly from these data. Accordingly, this characteristic is indicated by a continuous measure of the percentage of tertiary degree holders (ISCED6/7) in the total population aged 25 to 65. Using measures from independent education statistics yields the same substantive conclusions (available from the author).

Training & Adult Education: Again taking advantage of the fact that the IALS data are nationally representative, this variable consists of the share of respondents indicating that they have participated in professional training or adult education within the preceding year.

GDP: GDP per capita data from the year 1990 (in constant 2000 US dollars) stem from the Worldbank Indicators database. This and the remaining indicators of macroeconomic context are not available separately for Northern Ireland. I assign Great Britain's value, but analyses without Northern Ireland yield virtually the same results.

Industrialization: Following Treiman and Yip (1989), this index is based on the percentage of the population that does not work in agriculture and the per capita energy consumption for the 1970s (averaged and mean mean-standardized). Due to missing data for Slovenia (at that time still part of Yugoslavia), the index value cannot be constructed for this case and is instead imputed based on the GDP per capita value. Excluding Slovenia for this analysis does not change the results.

Gini: Gini coefficients of disposable household income come from the mid 1990s (depending on availability in each country) from OECD and UN-WIDER data sources.

Former Socialist: A dummy indicator for former socialist countries.

A.2 Alternative specification of institutional differentiation

Here, I report coefficients from alternative specifications of the effects of institutional differentiation as a sensitivity test for the findings reported in the main text. I draw on quantitative indicators of differentiation from published work by Brunello and Checchi (2007) (indicated by BC) as well as Horn (2009) (indicated by H; regressions based on these indicators exclude Chile and Slovenia, for which this information has not been assessed). Tables 4 and 5 follow the same sequence of models as that reported in Tables 1 and 7 but report only the coefficients for the different indicators of institutional differentiation. These indicators are:

• Age of Selection: The typical age of students at which educational differentiation begins. Indicators have been constructed for the 1980s (Brunello/Checci) and 1990s (Brunello/Checchi and Horn).
• Percent Tracked: The typical length of school tracking, measured as the share of time spent in a differentiated context on the primary and secondary level (for details see Brunello and Checchi 2007 : p. 798), again constructed for both the 1980s and 1990s.
• Number of School Types: The number of school types or distinct educational programs available for 15-year olds as reported by the OECD ( Horn 2009 )

A.3 Institutional and macro-economic context of high and low quality

Institutional and macro-economic context of quality at the top (90th percentile)

1 A long line of research in economics continues to debate the effects of being grouped with individuals of different ability levels ( Sacerdote 2011 ). In particular, it remains unclear whether heterogeneous peer effects – i.e., the varying impact of peers across the ability distribution – sum up to positive or negative average effects ( Summers and Wolfe 1977 ; Argys et al. 1996 ; Zimmer and Toma 2000 ; Brunello et al. 2012 ).

2 The Netherlands have to be excluded from this analysis due to irregularities in the coding of educational degrees; Scottland is excluded due to the low number of cases but would otherwise constitute an interesting comparative case on its own (see Raffe et al. 1999 ); the restriction to West Germany is done by excluding individuals who grew up in the former German Democratic Republic, who by themselves are too small a group to be analyzed separately.

3 Since these latter sensitivity analyses rely on only 11 out of the 19 nations included here, the results are subject to a much lower level of statistical precision and not reported here (available from the author).

4 Brunello and Checchi (2007) have used the IALS literacy measures to investigate cross-national differences in educational quality conditional on individuals' educational attainment. In general, it seems unclear what quality measures that have been purged of the effects of educational participation mean. The conceptual approach advanced ere, at least, advises against this strategy since it focuses on educational quality as the direct outcome of individuals' educational participation.

5 An additional replication of this analysis based on the ISSP-1999 data is also available from the author.

6 I note that the assessment of statistical significance in comparative research does not aim at making inferences to a larger population of countries. The relation of coefficients and their standard errors nevertheless indicates the degree of confidence that we can hold in claiming these effects to be meaningful, that is, not just due to random error (see Kenworthy 2007 ).

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Here’s How Data Can Help Unlock Education Equity

Tc’s renzhe yu, alex bowers, and youmi suk break down their ongoing, different approaches to the same goal: high quality education for all.

Now more than ever, educational equity — ensuring all students have access to meaningful educational opportunities, from college preparation and career assistance to support resources to civic participation — is crucial across America. However, the journey towards educational equity demands a multifaceted approach, with cross-collaboration and data at the helm. That’s where a core aspect of TC’s educator preparation and overall ethos comes into play, seeking to narrow the opportunity gaps millions of U.S. students face. 

While The Center for Educational Equity , established in 2005, focuses on research and policy around fair school funding and civic participation, three TC faculty members are finding unique ways to leverage data for equity. Renzhe Yu , Assistant Professor of Learning Analytics and Educational Data Mining, is leveraging data analytics to uncover the unintended consequences of the rapid adoption of generative artificial intelligence. Alex Bowers , Professor of Education Leadership, is showcasing the power of learning analytics and interoperable data sets to identify and address critical indicators of equity. Youmi Suk , Assistant Professor of Applied Statistics, is harnessing big educational data and cutting-edge machine learning methods to address questions about equity and fairness in educational practice.

Renzhe Yu, Assistant Professor of Learning Analytics and Educational Data Mining; Alex Bowers, Professor of Education Leadership; Youmi Suk, Assistant Professor of Applied Statistics (Photo: TC Archives)

• To reveal the bias and unintended consequences of generative artificial intelligence , Renzhe Yu performs large-scale data analytics.
• In order to identify issues of equity in a transparent way, Alex Bowers utilizes learning analytics and public data.
• Working to improve test fairness and curriculum planning , Youmi Suk draws connections between psychometrics, causal inference and algorithmic fairness.

(Image: iStock)

How Data Analytics Can Address the Growing Digital Divides

Stemming from Yu’s interest in learning how to “equip ourselves to better address existing issues related to education inequity,” his most pressing research focuses on understanding how the mass adoption of generative artificial intelligence has exacerbated digital divides in schools and institutions. Explored in a forthcoming working paper, the project uses large-scale text data from the education system to examine differences in everyday teaching and learning experiences as well as institutional attitudes toward generative AI.

“There are students who are more tech-savvy, there are instructors who are more experienced in using technologies, there are institutions that are more open-minded…and they have probably taken good advantage of ChatGPT and other generative AI tools in the past year,” explains Yu. But there’s also a significant number of students, parents, instructors, and institutions that don’t have that kind of access or awareness. “Although it’s just one year, the emergence of this technology may have widened these gaps,” says Yu.

To explore this growing divide, Yu and his research team focused on real-world data sources instead of conducting lab-controlled experiments in order to see how these relationships are playing out in real life. Because of his familiarity with the tech industry and the still-common impulse to innovate without considering the way that entire populations can be left behind, Yu says, “it’s really important to identify these unintended consequences in the early phase of life for these technologies.”

Yu’s other research interest in algorithmic bias — where he has long been exploring how algorithms used for decision making are treating learners differently based on race or other socio-demographic markers — is also made more urgent by the emergence of generative AI tools because if biased algorithms are “having dynamic conversations with students, [as is the case with generative AI,] the negative consequences of any bias in the process would be even more concerning.”

Ultimately, Yu hopes that his work provides perspective that is often ignored in the innovation process in order to create an education system that achieves equity with the help of advanced technology. 

How Data Can Inform Equity Efforts in School Policy and Conversation

Meanwhile, Bowers is looking at new ways school leaders can use reliable, evidence-based data practices to support equity efforts in schools nationwide. “One of my goals is to help bring communities together around the data that already exists for them—that’s already available, and help empower those communities,” he explains.

His recent work focuses on building collaboration with urban schools to identify data-driven equity practices and outcomes in education. In using a multidimensional framework, Bowers is hoping to facilitate more meaningful discussions with school communities by moving away from stigmatizing variables like standardized test scores and graduation rates.

“I think school districts are excited to have a definition of equity that they can bring into these community conversations, both with the school board, but also with teachers, parents, students.”

The project is fueled by his earlier research , which explores the value of interoperable, equitable datasets, along with a report that he co-authored with the National Association of Elementary School Principals (NAESP). The comprehensive report details the 16 indicators for assessing equity in education, including academic outcomes like test scores, graduation rates, behavioral data, and opportunities such as student engagement, access to quality learning, pre-K experiences, and more.These indicators give administrators and teachers a more transparent lens to examine school performance.

“It can help us move into a framework of, "How are we serving our students?" "Are we serving our communities?" It's moving away from fixating on the gaps and the outcomes and [instead] trying to problem solve as a collaborative opportunity through which we can bring in existing data.”

How Interdisciplinary Approaches to Analyzing Data Can Promote Fairness

For clearer reading.

Causal Inference: An interdisciplinary subfield that determines the cause of an observed effect by considering assumptions, design and estimation strategies.

Psychometrics: A subfield of psychology centered on theories and applications of measurement, assessment and testing.

A leading researcher exploring test accommodation effectiveness, Suk takes a multi-pronged approach to her main research goal of “developing and applying quantitative methods to address practical and important problems in the educational, social, and behavioral sciences.” One of her central projects is forging a connection between test fairness, a field of study that has been developed over 60 years, and algorithmic fairness, an emerging field with high stakes as algorithmic models are utilized in all aspects of life. 

“We can leverage the people, the methods and the concepts developed in test fairness in order to facilitate understanding of algorithmic fairness,” says Suk who is incorporating psychometrics and causal inference concepts into her work. “And it can go both ways. If there's any new discussion happening around algorithm fairness, we can leverage that discussion to make assessments and tests fairer.” As a part of this work, Suk is crafting new frameworks to investigate test fairness on the individual level instead of on the group level, based on the discussions on individual fairness within the algorithmic fairness research.

Her work is also directly informing her recent research on fair and personalized math curriculum recommendations for high school students, funded by the National Science Foundation. It’s known that students get the most benefit from personalized recommendations but “we have to be aware there may be some unconscious bias [in the recommendations],” explains Suk. To address this, Suk is applying algorithmic fairness constraints to create more equitable recommendations for high school students.

Through her varied research, Suk ultimately hopes to “create equitable and fair testing environments for all students and personalized curriculum plans that empower every student to succeed.”

— Sherri Gardner and Jaqueline Teschon

Tags: Evaluation & Learning Analytics Bias Education Leadership Evaluation & Learning Analytics

Programs: Applied Statistics Cognitive Science in Education Education Leadership Learning Analytics Measurement and Evaluation

Departments: Human Development Organization & Leadership

Published Monday, Apr 22, 2024

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Job quality impacts wellbeing more than education, income or gender

26 April 2024

The impact of job quality on wellbeing is of a similar magnitude to that of health, outshining more traditional factors, finds a report co-authored by Professor Francis Green and Dr Sangwoo Lee.

The importance of job quality is more significant than determinants that include education, gender, marital status, parental status, age or household income. 

The findings indicate that job quality tends to significantly influence wellbeing more for men than for women. In Europe, job quality accounted for a 14–19% variation in wellbeing. 

The research was undertaken with funding from the Economic and Social Research Council (ESRC) in collaboration with Min Zou (University of Reading) and Ying Zhou (University of Surrey). 

It explored the experiences of employed people in Europe, the United States, Australia and South Korea, covering 39 countries using global survey series that collect data on job quality in these areas. 

The European Working Conditions Survey (EWCS) surveyed 28 countries who were EU members at the time, and an additional seven European countries in 2015. The EWCS covered job quality more fully and more recently. South Korea and the USA closely followed the European questionnaire – the American version additionally surveyed respondents twice, in both 2015 and 2018. 

In comparison, the surveys used for Britain (Skills and Employment Survey) and Australia (Household, Income and Labour Dynamics in Australia) were less comprehensive in covering job quality. 

The research aimed to enhance a lack of existing data and focus on job quality in a way that is geographically extensive. 

The study underscores the need to prioritise job quality in socioeconomic policies, reassess the way it is measured, and support the gathering of evidence around job quality globally.

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• Read the report: Work and life: the relative importance of job quality for general well-being, and implications for social surveys
• Professor Francis Green’s UCL profile
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Subject report series: religious education

An Ofsted subject report into factors that influence the quality of religious education (RE) in schools in England.

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Deep and meaningful the religious education subject report.

This subject report evaluates the common strengths and weaknesses of religious education (RE) in the schools visited.

The report shares what we’ve learned and builds on the Ofsted religious education research review which identified factors that can contribute to high-quality school religious education curriculums, the teaching of the curriculum, assessment and systems.

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Women now outnumber men in the U.S. college-educated labor force

Women have overtaken men and now account for more than half (50.7%) of the college-educated labor force in the United States, according to a Pew Research Center analysis of government data. The change occurred in the fourth quarter of 2019 and remains the case today, even though the COVID-19 pandemic resulted in a sharp recession and an overall decline in the size of the nation’s labor force.

Today, there are more women ages 25 and older with a bachelor’s degree or more education in the labor force than before the pandemic: 31.3 million in the second quarter of 2022, compared with 29.1 million in the same quarter of 2019. The number of college-educated men ages 25 and older in the labor force is also greater than before the pandemic – 30.5 million, up from 29.1 million – though their ranks have not increased as quickly as those of women.

In 2019, women were on the cusp of overtaking men in the ranks of the college-educated labor force. The COVID-19 recession resulted in millions of Americans leaving the workforce , but it had disparate impacts on men and women, as well as on different industries and occupations. Two years into the recovery, Pew Research Center conducted this analysis to assess the progress of women toward the milestone of becoming a majority of the college-educated labor force.

Labor force estimates and participation rates are derived from the Current Population Survey (CPS) monthly files, sponsored jointly by the U.S. Census Bureau and the U.S. Bureau of Labor Statistics. The CPS is the nation’s premier labor force survey and is the basis for the monthly national unemployment rate released on the first Friday of each month. The CPS is based on a sample survey of about 60,000 households . The estimates are not seasonally adjusted.

The CPS microdata files analyzed were provided by the Integrated Public Use Microdata Series (IPUMS) at the University of Minnesota.

The COVID-19 outbreak affected data collection efforts by the U.S. government in its surveys, especially in 2020 and 2021, limiting in-person data collection and affecting the response rate. It is possible that some measures of labor market activity and how they vary across demographic groups are affected by these changes in data collection.

The pandemic disproportionately impacted labor market activity for adults without a bachelor’s degree, especially among women . The number of women with some college or less education in the labor force has declined 4.6% since the second quarter of 2019, compared with a smaller change among men with some college or less education (-1.3%).

The upshot of these disparate changes in the labor force by gender and education is that women have increased their representation in the college-educated labor force since 2019. At the same time, there has not been much change in the gender composition of the labor force that has some college or less education.

Changes in the composition of the U.S. population, along with changes in labor force participation, help account for these trends. The number of women and men in the labor force depends on the size of each group and the percent of that group who are working or seeking work.

The number of women and men in the U.S. with at least a bachelor’s degree has increased since the second quarter of 2019. But the share of college-educated women who are in the labor force has not changed since before the pandemic, while the share of college-educated men who are working or looking for work has declined.

In the second quarter of 2022, the labor force participation rate for college-educated women was 69.6%, the same as in the second quarter of 2019. In contrast, men and most other educational groups now have lower rates of labor force participation than they did in the second quarter of 2019.

This shift in the college-educated labor force – as women now comprise a majority – comes around four decades after women surpassed men in the number of Americans earning a bachelor’s degree each year.

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Richard Fry is a senior researcher focusing on economics and education at Pew Research Center

For Women’s History Month, a look at gender gains – and gaps – in the U.S.

Women have gained ground in the nation’s highest-paying occupations, but still lag behind men, how americans see the state of gender and leadership in business, single women own more homes than single men in the u.s., but that edge is narrowing, diversity, equity and inclusion in the workplace, most popular.

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More From Forbes

A college degree contributes in a major way to a healthier, longer life.

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Graduates wear a black hat to stand for congratulations on graduation

This story is part of a series on health inequities in the United States and their impact on length of life. These articles will focus specifically on factors related to poverty, race, and geography.

In an increasingly competitive job market, many recent studies have found that a college degree, which was once an entry level requirement for a well paying job, may not be as important as in years past. In fact, many workers without higher education degrees are finding that they’re able to enjoy comparable salaries to those with degrees without the ever growing burden of student debt. According to a recent LinkedIn study, nearly 70% of American jobs require a bachelor’s degree, while only 37% of the workforce has one. Some states, including the state of Maryland, have stripped bachelor’s degree requirements from job postings altogether, including those for government positions.

These trends, however, may be overlooking the large impact a college degree has on personal health. Studies of health inequities have shown that college graduates have tend to have greater access to healthcare, greater salaries, safer jobs, and safer housing than those without which contributes to longer, healthier lives.

Education and Employment

Although those without degrees are increasingly finding broader employment opportunities, those who hold college degrees typically have greater access to healthcare because they have jobs with higher incomes and better health insurance.

As many health inequity studies show, poverty and lack of health insurance can have very large negative impacts on health. Those with higher degrees often have higher self reported health ratings and lower rates of heart disease, depression, and diabetes than those with up to a high school diploma. Those who live in poverty, however, tend to have worse health outcomes and are often unable to pursue college degrees due to the rising cost of education. In 2020, about 25% of adults without a high school diploma were in poverty, compared with just 4% of those with at least a bachelor’s degree. During that same period, about 30% of adults with less than a high school degree were uninsured compared to only 5% of those with a bachelor’s degree or higher.

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Education not only affects your salary and health insurance status, but also the industry that you work in. During the early years of the COVID pandemic, those without college degrees were most likely to be essential workers and have greater exposure to the virus and therefore, a greater chance of getting sick. One study found that during the first year of the pandemic those with less than a high school degree had five times the risk of dying from COVID than those with postgraduate degrees.

Most essential occupations are low-wage and have a high share of workers below or near the poverty ... [+] line.

Education and Housing

Higher levels of education also come with safer and more secure housing. Adults with less than a bachelor’s degree are more likely to live in substandard housing which can increase exposure to toxins including lead and mold. Those with less education are also more likely to live in heavily polluted areas such as those near airports and major highways. The poorer air quality and increased exposure to toxins often lead to higher rates of respiratory illness. One study found that residents who lived within six miles of a major California airport had 17% more hospital admissions for asthma and chronic obstructive pulmonary disease (COPD) than the state average.

Education, Health Literacy, and Self Advocacy

Higher education is also associated with greater health literacy or the ability to find, understand, and use health information to make healthier decisions. Greater health literacy is associated with behaviors including higher vaccination rates and better consistency in taking prescribed medication. Health literacy also helps patients advocate for themselves and their needs. In some cases, the ability to make your own health decisions can be the difference between life and death. Research studies have found that women who advocate for the inclusion of doulas and midwives in their medical care team are up to 40% less likely to have a cesarean birth. This greatly reduces the chance of deadly post birth complications including blood clots, which are three times more likely after a cesarean birth.

Along with other social determinants of health including poverty and health insurance access, inequities in education are closely tied to inequities in health. As the landscape of the United States economy changes, however, many who have been systematically barred from pursuing higher education are now finding Bachelor degrees increasingly obsolete. Still, this shift has not been enough to overcome the poverty gap and its resulting health inequities. As such, ensuring equal access to education by expanding affordability of college and providing students with better resources will be an important tool to close that gap.

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