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Education is not just a tool for personal advancement, it is a fundamental lever for social mobility, economic growth, and social equality. It equips individuals with the knowledge and skills necessary for participation in the labor market, personal empowerment, and civic engagement. Globally, education is regarded as one of the most powerful tools for combating poverty, reducing inequality, and improving living standards. However, despite widespread recognition of its transformative potential, education remains unequal in many parts of the world.

While some children enjoy uninterrupted access to quality education, others face significant barriers—ranging from poverty, gender, race, and geographic location—to educational opportunity. These disparities are not just about the availability of schools or teachers, but about the resources available to students, the quality of instruction, and increasingly, access to technology. The digital divide, the gap between those with access to technology and those without, has become one of the most pressing issues in achieving SDG 4 (Quality Education), especially as education systems increasingly rely on digital tools and platforms.

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The Growing Problem of Digital Inequality

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The Growing Problem of Digital Inequality

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The advent of digital education has brought both great promise and great challenges. The COVID-19 pandemic intensified these challenges by shifting traditional, in-person education to online platforms across the globe. This rapid transition to remote learning underscored the digital inequality between students with access to technology and those without. Low-income families, particularly those in rural and underdeveloped regions, found themselves excluded from educational opportunities as a result of insufficient access to devices, internet connectivity, and digital literacy skills.

The digital divide has created a new form of inequality within the education system, one that compounds existing socio-economic disparities. As more educational content becomes available online, the gap between those who can access these resources and those who cannot is growing wider. The lack of access to technology is more than a mere inconvenience—it significantly impacts students' academic success and their future opportunities. According to a report by Stelitano et al. (2020), the shift to remote learning during the pandemic revealed that students without access to reliable internet or digital devices faced substantial learning losses. The divide is not limited to technology access; it also extends to the skills needed to navigate the digital learning environment. These challenges prevent many students, particularly from low-income households, from fully participating in their education, which ultimately undermines their long-term success.

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SDG 4, which seeks to "ensure inclusive and equitable quality education and promote lifelong learning opportunities for all," aims to address the educational inequality that exists globally. However, the digital divide represents a significant barrier to the achievement of this goal. As education becomes increasingly dependent on technology, the digital gap is preventing millions of students from receiving the education they deserve. Achieving SDG 4 requires closing this gap, which means ensuring that all students have access to the technology and resources necessary for a quality education. The ethical responsibility falls not only on governments but also on the private sector and educational institutions to create an inclusive and equitable education system that leaves no one behind.

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Why Quality Education Matters - Sustainable Development Goal 4

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This Padlet will explore the ethical challenges of digital inequality in education, examining how it affects students from low-income and marginalized communities, and how various stakeholders must come together to address this growing issue.

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This study explores the impact of the COVID-19 pandemic on educational inequalities, focusing on the digital divide that has worsened during this period. The research highlights that students from low-income and disadvantaged backgrounds faced greater learning losses during the transition to remote learning due to a lack of access to digital resources, such as computers, tablets, and internet connectivity.

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Students in low-income communities experienced greater learning losses, particularly in critical subjects such as math, reading, and writing.

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Wealthier students, who had better access to technology, continued their education with fewer interruptions. This digital advantage exacerbated the educational divide.

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The study calls for immediate interventions from governments, corporations, and educational institutions to address these disparities and ensure that all students have the resources they need to succeed.

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Utilitarianism: Maximizing the Greatest Good

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Utilitarianism: Maximizing the Greatest Good

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Utilitarian ethics focuses on actions that promote the greatest good for the greatest number of people. In the context of education, this would mean allocating resources to ensure that the most underserved students have the tools they need to succeed. Governments and businesses should prioritize reducing educational inequality by providing digital tools to students who lack them. This would benefit not just individual students, but society as a whole, by improving overall educational outcomes and creating a more equitable society.

Example: Governments could provide free access to digital devices and internet subsidies for students in low-income communities, ensuring that a large group of students can access quality education, thereby benefiting society.

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 Kantian Ethics: Education as a Fundamental Right

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 Kantian Ethics: Education as a Fundamental Right

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Kantian ethics emphasizes that education is a fundamental human right that should be accessible to everyone, regardless of their socio-economic background. From this perspective, denying access to education due to the digital divide is a violation of human dignity. Kantian ethics argues that society has a duty to ensure equal access to education for all children. Denying students in underserved communities access to digital education tools and resources denies them their basic right to education.

Example: According to Kantian principles, if a child is deprived of education because they lack a laptop or internet access, it is not just an inconvenience—it is a moral failure on the part of society.

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Virtue Ethics: Fairness and Justice in Education

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Virtue Ethics: Fairness and Justice in Education

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Virtue ethics focuses on moral virtues such as fairness, compassion, and justice. From this perspective, it is not enough to provide basic resources to children; governments and institutions must distribute resources fairly and ensure that no child is left behind because of their socio-economic status. The focus should be on fostering justice in education, where all students, regardless of background, have the same opportunity to succeed.

Example: By ensuring that all students have access to digital learning tools, school districts and governments can demonstrate fairness and commitment to providing equitable education opportunities for all.

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Social Contract Theory: Society’s Responsibility to Provide Education

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Social Contract Theory: Society’s Responsibility to Provide Education

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Social contract theory holds that society has a collective responsibility to ensure that all members are afforded essential rights, such as education. In this case, if certain children are unable to access education because of the digital divide, then society is failing to live up to its obligations to its members. Social contract theory would argue that governments and corporations must act to ensure that equal access to education is provided for all students, particularly those who are most vulnerable.

From a social contract perspective, failing to address the digital divide undermines the social contract, as it denies equal educational opportunities and further entrenches social inequalities. Governments must provide the necessary resources to ensure universal access to education, especially in the digital age.

Example: Governments must invest in providing universal broadband and technology subsidies for underserved communities. This is not just an investment in education, but an investment in fulfilling society's social contract with its citizens.

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Job Description:
An Educational Technology Specialist focuses on integrating technology into the classroom to enhance the learning experience for students and educators. They work to ensure that schools and other educational institutions have the digital tools and platforms needed to engage students in a modern, digital-first learning environment. This role involves selecting, implementing, and maintaining technology solutions that help students achieve better learning outcomes, especially in regions where access to traditional educational resources is limited.

Responsibilities:

Assist in the selection and implementation of digital learning platforms and tools.

Provide technical support to teachers and students on using digital devices and platforms for learning.

Collaborate with educators to create interactive and engaging digital content.

Ensure accessibility of digital tools for all students, including those with disabilities.

Train teachers and administrators on effective use of technology in the classroom.

Skills Needed:

Expertise in educational technology and e-learning platforms.

Strong communication skills to collaborate with educators and stakeholders.

Problem-solving skills to troubleshoot technical issues.

Knowledge of digital accessibility and how to ensure that online learning tools are accessible to students with diverse needs.

Example: An educational technology specialist might work for a school district to implement a 1:1 device program, where every student receives a laptop or tablet to use for the entire school year. This helps to ensure that all students have access to the same educational resources, regardless of their home environment.

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Job Description:
A Digital Literacy Trainer is responsible for developing and delivering programs that teach digital skills to students, teachers, and even parents. This role is crucial in ensuring that individuals of all ages and backgrounds have the skills necessary to engage with digital education tools and online learning platforms effectively. Digital literacy training is essential for ensuring that no one is excluded from the opportunities provided by digital education, especially in areas where access to technology may be limited.

Responsibilities:

Develop training materials and curricula focused on digital literacy.

Teach students, teachers, and community members how to use digital tools (such as computers, smartphones, and the internet) effectively for learning.

Assess the digital literacy levels of students and offer personalized support to those who need it.

Stay updated on new digital tools and trends in digital education and incorporate them into training programs.

Collaborate with educational institutions and organizations to create accessible digital literacy courses.

Skills Needed:

Strong knowledge of digital tools and online platforms.

Ability to simplify complex digital concepts for diverse audiences.

Experience in curriculum development and instruction.

Patience and the ability to work with learners at various skill levels.

Example: A digital literacy trainer might be hired by a non-profit organization working to provide digital education to rural communities. They might design a program that helps students and teachers in those areas learn how to use online learning platforms like Google Classroom or Zoom.

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CSR (Corporate Social Responsibility) Manager for Education and Technology

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CSR (Corporate Social Responsibility) Manager for Education and Technology

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Job Description:
A CSR Manager in the education sector works for corporations that aim to give back to communities by focusing on education and technology access. In this role, you would oversee programs designed to bridge the digital divide and help underserved communities gain access to digital education tools. CSR initiatives might include donating computers, providing internet subsidies, or creating partnerships with educational institutions to improve educational outcomes for marginalized groups.

Responsibilities:

Develop and manage CSR initiatives related to digital education, including providing technology donations to schools in need.

Partner with NGOs and government organizations to increase education access in underserved communities.

Lead internal programs to promote digital literacy among employees, which can then be passed on to the communities they serve.

Monitor and assess the impact of the CSR initiatives on education equality and digital inclusion.

Ensure that CSR projects are aligned with the company’s values and contribute to achieving SDG 4.

Skills Needed:

Strong understanding of corporate social responsibility and sustainable business practices.

Experience in partnership building and working with NGOs and government bodies.

Knowledge of the education sector, particularly in relation to digital tools and access to technology.

Ability to manage budgets and assess the effectiveness of CSR projects.

Example: A tech company like Microsoft might hire a CSR manager to oversee its partnership with schools to provide free laptops to underserved students. They could also set up trainings for teachers to improve digital literacy in rural areas.

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RAND Corporation Report: The Digital Divide and COVID-19

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RAND Corporation Report: The Digital Divide and COVID-19

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This report explores how the COVID-19 pandemic worsened educational inequalities by highlighting the lack of internet access in high-poverty and rural schools. Teachers from these schools reported that many students were unable to participate in remote learning due to limited access to devices and internet connections. The study emphasizes the need for policymakers to improve infrastructure, ensuring that all students, regardless of their socioeconomic background, can engage in online education(Stelitano et al., 2020).

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OECD Report on Digital Equity and Inclusion

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OECD Report on Digital Equity and Inclusion

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The OECD report discusses how digital tools can either support or hinder learning, depending on how well they are distributed and implemented. It focuses on the need for equitable access to technology to ensure that students from diverse backgrounds can benefit from digital education. The report outlines policies aimed at promoting digital inclusion in schools, ensuring that all students have the opportunity to succeed, regardless of their access to technology(OECD, 2020).

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Haelermans, C., Korthals, R., Jacobs, M., de Leeuw, S., Vermeulen, S., van Vugt, L., et al. (2022). Sharp increase in inequality in education during the COVID-19 pandemic. PLOS ONE, 17(2), e0261114. https://doi.org/10.1371/journal.pone.0261114

OECD. (2020). Digital equity and inclusion: Challenges and opportunities for technology in education. OECD. Retrieved from https://www.oecd.org

Stelitano, L., Doan, S., Woo, A., Diliberti, M. K., Kaufman, J. H., & Henry, D. (2020). The digital divide and COVID-19: Teachers' perceptions of inequities in students' internet access and participation in remote learning. RAND Corporation. https://www.rand.org/pubs/research_reports/RRA134-3.html

UNESCO. (2020). Education in a post-COVID world: Nine ideas for public action. UNESCO. Retrieved from https://en.unesco.org/covid19/educationresponse/nine-ideas

United Nations. (2024). United Nations Sustainable Development Goals: Goal 4 - Quality Education. Retrieved from https://sdgs.un.org/goals

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This section provides definitions for critical terms related to digital inequality and SDG 4 to help readers unfamiliar with the topic. These definitions will ensure that the concepts discussed throughout the Padlet are understood in their full context.

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Refers to the gap between individuals, households, and communities that have access to modern information and communication technology (ICT) and those who do not. This divide often results from factors like socio-economic status, geographic location, and availability of infrastructure.

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Part of the United Nations Sustainable Development Goals, SDG 4 aims to "ensure inclusive and equitable quality education and promote lifelong learning opportunities for all." It focuses on universal access to education and quality in learning.

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The unequal distribution of educational resources, opportunities, and outcomes, often due to factors such as socio-economic status, geography, and access to technology.

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A method of education where instruction is delivered online rather than in-person. Remote learning has become a widespread alternative during the COVID-19 pandemic but has disproportionately affected those without access to digital resources.

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The ability to effectively and critically use digital tools, such as the internet, computers, and other devices, for learning and problem-solving.

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Having the necessary digital tools (computers, tablets, etc.) and internet connectivity to engage in online learning and other educational activities.

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The principle that all individuals, regardless of background, should have equal access to technology and the internet to engage in educational, social, and economic activities.

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The decline in academic performance due to interruptions in formal education, especially when students do not have access to the tools and resources needed for continuous learning.

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The pursuit of fairness and equality in society, including equitable access to resources and opportunities, such as education.

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Measures of academic achievement and success, such as grades, graduation rates, and the acquisition of skills, which can be influenced by access to technology and learning resources.

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Equity refers to fairness in providing resources based on individual needs, while equality means treating everyone the same, regardless of their starting point or needs.

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The hardware, software, and network systems necessary to support digital education.

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 Sharp Increase in Inequality in Education During the COVID-19 Pandemic

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 Sharp Increase in Inequality in Education During the COVID-19 Pandemic

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Published in: PLOS ONE (Haelermans et al., 2022)
Link: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0261114

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Carla Haelermans,

Roxanne Korthals,

Madelon Jacobs,

Suzanne de Leeuw,

Stan Vermeulen,

Lynn van Vugt,

Bas Aarts,

Tijana Prokic-Breuer,

Rolf van der Velden,

Sanne van Wetten

Subject Areas

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Abstract

The COVID-19-pandemic forced many countries to close schools abruptly in the spring of 2020. These school closures and the subsequent period of distance learning has led to concerns about increasing inequality in education, as children from lower-educated and poorer families have less access to (additional) resources at home. This study analyzes differences in declines in learning gains in primary education in the Netherlands for reading, spelling and math, using rich data on standardized test scores and register data on student and parental background for almost 300,000 unique students. The results show large inequalities in the learning loss based on parental education and parental income, on top of already existing inequalities. The results call for a national focus on interventions specifically targeting vulnerable students.

Citation: Haelermans C, Korthals R, Jacobs M, de Leeuw S, Vermeulen S, van Vugt L, et al. (2022) Sharp increase in inequality in education in times of the COVID-19-pandemic. PLoS ONE 17(2): e0261114. https://doi.org/10.1371/journal.pone.0261114

Editor: Jérôme Prado, French National Center for Scientific Research (CNRS) & University of Lyon, FRANCE

Received: July 19, 2021; Accepted: November 25, 2021; Published: February 2, 2022

Copyright: © 2022 Haelermans et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data cannot be shared publicly because they are part of the data at Statistics Netherlands and cannot be exported from the secured virtual environment at Statistics Netherlands. However, all data underlying the results presented are available at Statistics Netherlands and use can be requested by national and international researchers via the Netherlands Cohort Study on Education (NCO) and Statistics Netherlands; data access requests may be sent to info@nationaalcohortonderzoek.nl. An extensive description of the data and the access procedure can be found in Haelermans, C., T. Huijgen, M. Jacobs, M. Levels, R. van der Velden, L. van Vugt and S. van Wetten (2020). Using Data to Advance Educational Research, Policy and Practice: Design, Content and Research Potential of the Netherlands Cohort Study on Education. European Sociological Review, 36(4), 643-662, https://academic.oup.com/esr/article/36/4/643/5871552?login=true.

Funding: The authors gratefully acknowledge financial support from The Netherlands Organisation for Health Research and Development (ZonMw, https://www.zonmw.nl/nl/) (project 10430 03201 0014). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Grant acquired by CH, RK, TP-B, RvdV, IdW.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The COVID-19-pandemic of early 2020 interrupted or even completely halted the learning of children in many countries around the world. Globally, schools were closed for an average of almost 95 school days between March 2020 and February 2021 [ 1], which is equivalent to almost half a school year in countries where a school year is 40 weeks. In many western countries, schools continued to teach remotely. However, there were many challenges related to distance learning, such as access to digital learning devices and digital learning gaps [e.g., 2- 4]. This prompted serious worries of social-emotional problems and learning loss. Despite the lack of adequate data in many countries, some studies appeared on the use of online learning tools by students [e.g., 5] and on the effect of distance schooling on performance and learning gains of students in primary education. Although some studies did not find significant learning losses [e.g., no effects on reading in the USA [ 6], no learning deficits on schools with a large share of students with advantaged backgrounds in Australia [ 7]], most studies report negative consequences of the school closures for children's educational development [Belgium [ 2], UK [ 4, 8], Italy [ 9], Switzerland [ 10], Germany [ 11], USA [ 12- 15], Norway [ 16]]. For higher education, the results are less consistent: some find negative effects [ 17] while others indicate that distance learning might have made students more efficient [ 18] or see little effects [ 19].

There are worries that some groups of students experienced lower learning gains due to the school closures and the COVID-19-pandemic than others. Our hypothesis is that the school closures and the pandemic resulted in increased inequality in skill development for students from specific backgrounds (socio-economic status, income and migration background). There is reason to believe that inequalities have indeed increased due to the school closures. For instance, in the Netherlands especially lower-educated parents felt less capable in helping their children with their schoolwork [ 20, 21]. In the United Kingdom, we see that middle class parents spent more time on home schooling than parents from the working class [ 22, 23]. If this is the case, and these learning losses persist, they can be detrimental for development of skills in the long run, and in turn lead to an increase of the existing inequalities in opportunities in education and on the labor market [ 24].

Previous studies on inequalities based on socioeconomic background variables in students' learning gains during the school closures, were hampered by data limitations. Some were limited by their data on educational performance: they used relatively small samples, focused on a specific region rather than a national representative sample, or were limited to only one grade level or subject [ 6, 8, 9, 12, 13, 16]. Others had limited information on students' background characteristics. They used school level indicators [ 2, 7, 11] or relatively uninformative categories. For example, a recent study based on Dutch data was only able to distinguish between families in which at least one parent had a lower secondary degree (92%) and families in which both parents had less than a lower secondary degree (8%) [ 3]. Our study improves upon these studies for several reasons: 1) as a result of the widespread use of standardized testing in the Netherlands, we have a large sample of students who were tested shortly before and after the first lockdown, 2) we have rich student background information at the individual level, including multiple student background variables that indicate whether a student is disadvantaged or not, based on meaningful and informative categories and 3) we focus on effects for separate grade levels, and three different subjects (reading, spelling and mathematics) showing large variation, instead of only looking at overall effects or one subject.

Therefore, in the study at hand, we are able to look in greater detail at background differences between students and present results showing that the learning loss due to the school closures are unequally distributed: students from disadvantaged backgrounds have suffered much more than their fellow students. To show this, we use standardized test score data from the Netherlands and link this to register data on student and parental background for primary school students.

COVID-19 educational policy changes in the Netherlands

Although compulsory education starts at age 5, Dutch children generally enter primary school at age 4. They remain in primary school up to age 12, after which they enter secondary school and are tracked according to their ability. Almost all schools in the Netherlands are public schools (99%) funded by the Ministry of Education, Culture and Science [ 25].

February 27, 2020 the first COVID-19 patient was reported in the Netherlands. Primary schools closed at March 16, 2020 and reopened May 11, 2020. Vulnerable children, and children of parents with essential occupations who could not work from home, were allowed to come to school during the school closure. However, these children usually followed the same program as the children who had to stay at home and comprised only around 5% of all children in this first period of school closure. Up to June 7, 2020 children only went to school half of the time. In this way, groups were smaller and it was easier to keep distance. From June 8 onwards schools went back their usual schedule. Children and teachers were still urged to stay at home when they showed any symptoms associated with COVID-19.

The Netherlands was relatively well equipped for online education, as a total of 96% of the Dutch households have internet access at home [ 26]. Additionally, the Dutch government made 2.5 million euros available in March to support online learning. This money was used to buy laptops and/or to provide internet access for 7,000 students. This money was supplemented with another 3.8 million euros in May 2020. In total, over 16,000 laptops and tablets were financed in this way [ 27]. Nevertheless, the school closure happened relatively sudden with no time to prepare. Teachers had to improvise, students suddenly had to structure their own school day, and parents had to act as teachers for their children. Although we do not know exactly how much education children received while schools were closed, there are strong indications that children spent less time on their education than usual. Studies in Germany and Switzerland report considerable reduction in studying time during school closings [ 28, 29]. Moreover, a survey among Dutch parents revealed that parents, especially in disadvantaged families, did often not feel equipped to support their children during the school closing [ 20].

Children in countries with longer school closings and less internet access might have experienced larger learning losses and larger inequalities because they experienced prolonged periods of limited and unequal excess to education. In line with this hypothesis, a recent study in Italy [ 9] reports larger learning losses (0.19 SD) than previous studies in the Netherlands (0.08 SD) [ 3]. In Italy schools were closed for 15 weeks (one of the first and longest school closings in Europe). Moreover Italy has one of the lowest share of households with a broadband connection [ 30] and 12% of the students between 6 and 17 years old did not have access to a computer or digital tools at home in 2018/2019 [ 31]. However, contradicting the idea that longer school closings result in larger learning deficits, a study in Belgium-where schools closed for 8.5 weeks-reports a reduction in mathematics scores of 0.19 SD [ 2] which is similar in size to the effects found in Italy (15 weeks). Altogether, more research based on country comparisons is needed to be able to state that longer lockdowns result in larger learning deficits and an increase in educational inequalities.

Materials

In the Netherlands, students take standardized tests throughout grades 1 to grade 6 in primary education. These standardized tests come from different suppliers, with the largest supplier being CITO, with which we collaborated for this paper. Furthermore, schools use administration systems to store the information about the standardized test scores. Three administration systems exported the data on standardized test scores from school year 2013/2014 onwards as part of the Netherlands Cohort Study on Education (NCO) project, a national project initiated by the Dutch Research Council (for a description of this project, see [ 32]). With permission of the schools, the administration system exports the data on the standardized test scores to Statistics Netherlands, who pseudonominize the student-id and school-id. Before any data was exported, parents were informed about the project and data export by the school, and were given the opportunity (during 4 to 6 weeks) to object against export of their child(ren)'s data (by informing the school written or orally). The school registered any objections in their administration system, and data was not exported from those students whose parents objected.

The data was collected over a period of three months with two exports from the administration systems, the first export took place on the 30th of November 2020, the second on the 18th of January 2021. In this export, information is collected from school years 2013/2014 to 2019/2020 and gradually consists of more and more students in more and more grades. For more information, see Table 1. In total, 1,319 schools and unique information of 291,635 students was gathered on standardized test scores. After cleaning the data, the total sample used for analyses of this paper comes down to 201,819 students in 1,178 schools.

National standardized test scores

From grade 1 to grade 6, students take standardized tests twice a year, a midterm test, most often administered to students in the months January and February of the school year, and an end-of-term test, mostly administered in the months June and July, right before the summer holidays. For most schools, these are digital tests. Some schools opt for the pen-and-paper version. Due to the school closure in the spring of 2020, for the school year 2019/2020 the end-of-term test could be postponed until after the summer holidays, which many schools did: about a quarter of schools decided to test their students after the summer holidays in August, September or even October. Test supplier CITO made a recalculation for the test scores in August, September and October to account for the extra time until the test, and make them comparable to the test scores of students who made the test before summer. Note that the tests written during the pandemic were exactly the same type and format as before the pandemic, and there is no within school variation between the type and format of the tests before and during the pandemic.

We use test scores in the domains reading, spelling and math. Table 2 shows the number of test records and unique students per domain. The test in math contains both abstract problems and contextual problems that describe a concrete task. The reading test assesses the student's ability to understand written texts, including both factual and literary content. Lastly, the test in spelling asks students to write down a series of words (no verbs), demonstrating that they have learned the spelling rules. For reading, there is no mid-term test in the first grade, therefore the learning gains between the midterm test and end-of-term test cannot be calculated for grade 1.

The learning gains are defined based on the standardized test scores and are calculated by subtracting the score on the midterm test from the end-of-term test of each domain within a school year, with the condition that the student must have taken a midterm and end-of-term test within the same school year at the same school. To remove the influence of outliers, the top and bottom 1% of the absolute learning gains scores are not included in the analyses.

Student background variables

In the secured virtual environment of Statistics Netherlands, standardized test scores can be matched to background information of the students and their parents. Note that the data in the environment of Statistics Netherlands are pseudonymized such that data are fully anonymous to the researchers that use these data. The data on background information that we use are the highest education level and highest income of parents, student migration background and student gender. Parental education is defined as low when the highest obtained degree of (one) the parents is in pre-vocational secondary education (vmbo b/k), or a degree in upper secondary vocational education (mbo 1), or grades 7 to 9 in pre-vocational secondary education (vmbo gl/tl) or senior general secondary education or university preparatory education (1), middle when a degree in upper secondary vocational education level 2, 3 or 4, or when completed senior general secondary education or university preparatory education (2), and high when a degree at a university of applied sciences is attained or higher (3). This division of parental education over three categories is also being used in the Netherlands Cohort Study on Education and leads to a division in categories that is not only relevant at the content level, but also provides us with large enough groups to have statistical power. Highest parental income is defined as low when the highest income of one of the parents is below the minimum income level (1), middle when the income is higher than minimal level but below twice the minimum income level (2) and high when the income of one of the parents is higher than twice the minimum income. Students' migration background is defined as either having a Dutch background or a western background, or a non-western background. Students with a Dutch or western background are combined into one category because the data contains only very few students with a western background, and the results of these two groups are very comparable. In terms of parental education and household income, students in our sample with a non-western migration background are more likely to come from households with relatively low educated parents (26% compared to 6% for the native Dutch and western migrant student sample) and a relatively low income (45% compared to 16%). Lastly, the gender of the student is defined as male or female.

Representativeness

The data on standardized test scores are only available for schools who gave permission to export the test scores from their administrative system to Statistics Netherlands. As a result, we do not have full population data and consequently selectivity of the sample might play a role. In the schoolyear 2019/2020, we had a total number of 6,174 primary schools in the Netherlands. The 1,178 schools in our sample therefore comprise a proportion of 19% of the total number of schools. Two main sources of selectivity into the sample can be identified. First, the schools that decided to participate in the data collection might not be random. In exchange for sharing the standardized test score, schools received a report on the performance of their school relative to other schools with a comparable student population. We can expect that active schools, which are keen to monitor their progress, are especially interested in the reports and more likely to participate in the data collection project. Second, not every student is tested. Schools tend to exclude students who are absent (e.g., due to illness) or have a very large learning loss. For these students, schools feel a test is not possible or useful. Usually, the number of students per school which are excluded from the standardized tests is relatively small. However in 2020, after the school closed for several weeks, more schools decided to skip the standardized tests for a larger share of the student population. It is reasonable to assume that students with larger learning losses are less often tested. Therefore, it is likely that our data is not representative for the whole population, and additional tests on our sample in comparison to the full population confirm this. Table 3 shows the representativeness of our sample in comparison to the full population (based on the National Cohort Study on Education; [in Dutch abbreviated as NCO] [ 32] on student and school background characteristics. Overall, we see that our sample is over-represented in students with a non-western migration background, and students with low parental income. Furthermore, schools in our sample tend to be larger schools located in more urbanized areas.

To limit the impact of selectivity and over-representation of certain students and schools, we use inverse probability weights. In calculating the weights, we use population data on all students enrolled in Dutch primary education and calculate the probability to be in our test score dataset separately per academic year, grade, and test subject domain as a function of students' observable characteristics. These characteristics are parental education, income, migration background, gender, percentage of students with low educated parents at the school, number of students at the school, urbanisation level (based on location of the school), province (based on location of the school) and school denomination.

Descriptive statistics

Table 4 shows the unstandardized learning growth for the three domains for the 2 years before the pandemic and the year of the pandemic separately. It also shows the learning growth split by group of parental education. Table 4 is used to calculate the normal average learning growth per week in the 20 weeks between midterm and end-of-term test, and the deviation from this in the COVID-19-year. For example, if the normal learning growth for reading is 7 (in 20 weeks time), and during the pandemic it's only 5, the decline in learning growth in weeks is (20-((20/7)*5) = 5.7.

Methods

In order to estimate the effect of the COVID-19 related school closure on students' learning gain, we compare the learning gain between the midterm and the end-of-term test of the COVID-19-exposed cohort (2019/2020) to the learning gain of students from the two previous cohorts using OLS regressions. To account for potential differences in observable characteristics between students of different cohorts, we add controls for student gender, student household income, migration background, and parental educational background. Further, since for some students of the 2019/2020 cohort the end-of-term test was postponed until the start of the next academic year, we add a dummy indicating whether the test was taken at the end of the 2019/2020 academic year or at the beginning of 2020/2021, resulting in the following regression equation, resembling a difference-in-differences design: (1)

Where Δ yij stands for the difference in achievement between the end-of-term test and the midterm for student i in grade j. Tij is an indicator for the COVID-19 exposed 2019/2020 cohort, X ij is a vector consisting of the aforementioned control variables, and εijs is the school-level clustered error term. β is our coefficient of interest, which captures the difference in average learning gain between the COVID-19 exposed 2019/2020 cohort and the average learning gain of the (pooled) preceding two cohorts (2017/2018 and 2018/2019).

Identification of the COVID-19 effect hinges on the assumption that the learning gain of the different cohorts would have followed a similar trend in the absence of the pandemic. While this assumption is fundamentally untestable, we can provide supporting evidence for it by looking at the variability of learning gains for all grades over time. If these trends are stable, we can be reasonably sure that the difference between the 2019/2020 cohort and the previous two cohorts was caused by the impact of the pandemic. The results of these analyses can be found in Figs 5- 10.

In order to estimate the heterogeneous impact of the COVID-19-pandemic along student background characteristics, we add an interaction between the treatment-dummy and the student characteristic of interest to the regression. This results in the following equation: (2)

Where Cij stands for one of the aforementioned student characteristics: gender, parental education, household income, and migration background. The vector of control variables X ij still includes all other student characteristics. As a robustness check, we also present results of analyses where apart from the interaction we do not include any of the other control variables, with similar results (see Tables 5- 10). Finally, a concern could be raised that some of the student characteristics we observe are capturing similar things. For example, parental education and household income are likely to be strongly correlated. In order to isolate the additional impact of COVID-19 along (for example) household income, we therefore run analyses where we control for the interaction between parental education and the treatment-dummy in addition to the interaction with household income. In addition to household income, we do this for student gender and migration background as well, resulting in the following equation: (3)

With Eij standing for the highest level of obtained parental education. As mentioned before, in our main specification we use inverse probability weighting to obtain results representative for the whole Dutch primary school population. As a robustness check, we run the same analyses without employing weights as well as using entropy-balancing weights ensuring covariate balance between the COVID-19 exposed cohort and the control cohorts (similar to the method used by [ 3]), and obtain similar results (see Tables 5- 10).

Results

In this section, we show the consequences for inequality during the COVID-19-pandemic by comparing the learning gains of students in pre-COVID-19 times (school years 2017/2018 and 2018/2019) to the learning gains since the COVID-19-pandemic (school year 2019/2020) by estimating Eqs ( 1) through ( 3). The results are presented in Figs 1- 4. Fig 1 presents the results of Eq ( 1), estimating the overall impact of COVID-19 on student learning gains. Fig 2 shows the result of Eq ( 2), estimating the disparate impact along students' parental education. Figs 3 and 4 shows the results from Eq ( 3), estimating the disparate impact along students' household income and migration background, in addition to the differences along parental education. Tables 11- 15 show all the underlying regression results.

Standardized coefficients of learning gains in COVID-19-year 2019/2020 of low-educated (zero line) versus middle- and high-educated.

Fig 1 shows that in all domains, students have a lower learning gains during the COVID-19-pandemic compared to the growth of students in previous cohorts. On average there is 0.14 standard deviations (SD) learning loss in reading, 0.15 SD in spelling and 0.21 SD in math. Looking at the different grades, we see a gradual increase in the learning loss from grade 1 to grade 5 onwards across all domains, with some outliers, like for instance spelling in grade 4. For reading, we see students experience about 0.06 to 0.20 SD learning loss compared to students from previous cohorts. Looking at spelling shows a similar result, where students experience about 0.13 to 0.18 SD learning loss. Math shows the largest deficits in learning with on average 0.13 (grade 1) to 0.33 (grade 5) SD learning loss.

Although most students learned less in 2019/2020 than their peers in previous cohorts, some students show larger learning loss than others, leading to (increasing) inequality between students. We look at four dimensions of inequality: (1) by parental education, (2) by family income, (3) by migration background and (4) by gender.

Fig 2 shows that children with low-educated parents learned less between the midterm and end-of-year test than their peers with high-educated parents, and that the differences are largest in grades 1, 2 and 3, and for spelling and math(note that alternative specifications in which we use four categories of parental education, or in which we use three categories which are not based on parental education, but on the indication (used for funding purposes) whether a child is a regular child, has a disadvantaged background or a very disadvantaged background, yield very similar results and the same conclusions.) The results show, for example, that children of high-educated parents experience about 0.1 SD more learning gains during the year 2019/2020 compared to children of low-educated parents. In other words, the learning loss due to school closings is larger for students of low-educated parents, and inequalities have grown because of this. The differences between students of high- and low-educated parents are statistically significant for spelling and math but not for reading, implying that the role of parental background on educational development during the first school closure due to the COVID-19-pandemic is largest for math and spelling, and less pronounced for reading comprehension. Altogether, these findings show that the existing differences in learning gains based on parental education prior to the COVID-19-pandemic have increased during the spring of 2020 when learning was disrupted. These increased differences based on parental education are not surprising, since students were more dependent on the help their parents could provide with schoolwork during the school closure. This finding is also confirmed by other studies: parents in the Netherlands with lower educational attainment felt less capable to help their children with schoolwork [ 20, 21].

Parental income also plays a role: Fig 3 shows that children from medium and higher income households increase their scores between the midterm and end-of-year test more strongly in the COVID-19-year than their peers from a family with a lower household income, with the largest effects in grades 2 and 3, and for spelling and math. For example, children from medium and high household income experience about 0.05 SD more learning gains than children in low-income households. Note that the relation between income and learning gains is additive to the additional role of parental education during the pandemic. We explicitly take into account the effect of parental education on learning gains and the additional role during the pandemic, and we still see an effect of household income during the school closure. However, it is not surprising that we find an effect of parental income on top of the effect of parental education: parents with higher household income were more likely to afford additional help for their children during their time at home. One study suggested that they provide more private access to additional online learning materials [ 33].

We also looked at the role of migration background, again on top of effects of parental education. The results in Fig 4 indicate that, conditional on the effect of parental education, overall, students with a non-western migration background did not perform significantly worse than other students (native and with a western migration background) during the COVID-19-pandemic. We only find a small significant result for math for grade 2 and 3, and for all grades taken together. Note that, if we do not condition on effects of parental education, we do find significant differences for migration background. Hence, overall, we find that the increased inequality during the pandemic is based on parental education and parental income rather than on migration background.

Lastly, we find that there are no significant differences for gender, neither with nor without controlling for the effect of parental education. Girls seem to perform slightly worse on reading and math but the coefficients are small and almost only statistically significant when all grade levels are taken together (see Table 14).

Robustness checks

Our preferred model, used throughout the main text, includes inverse probability weights to obtain results that are representative for the entire Dutch primary school population, and we compare the learning gains between the midterm and the end-of-term test in the COVID-19-year to the gains in the two years prior. Furthermore, in the analyses mapping the disparate impact of COVID-19 along several student characteristics we control for all other background characteristics. These choices could potentially influence our results and their interpretation. Therefore, in this section we show the results of additional analyses where we change the specification of our main model.

To demonstrate how the choice of including inverse probability weights influences the results, Tables 5 and 6 show the results when using entropy-balancing weights and unweighted regressions, respectively. While there are some slight differences in terms of significance levels of certain coefficients, the overall pattern of lower learning gains during the COVID-19-year for students from low-income households and low educated parents, especially in spelling and math, remains similar in magnitude.

In Table 7 we run our main specification without controlling for student gender and migration background, and the dummy accounting for whether the end-of-term test was taken at the end of the school year of 2019/2020 or at the beginning of the 2020/2021 school year. It could be that the interaction effects found on student household income and parental educational background only hold conditional on these other student characteristics. If so, this complicates the interpretation of our results. Fortunately, the exclusion of these additional control variables does not change the found associations.

Table 8 shows how the results change when we control for students' learning gains that they obtained in the previous year. Including prior performance helps in addressing potential differences between cohorts in the trend of their cognitive development. However, the downside of this specification is that we do not observe prior performance for students that are in the first grade (or second grade for the reading domain), and they drop from the analyses as a result. For the other grades, the results are similar to the main specification. Prior learning gains are significantly positively related to later learning gains for all but one subgroup (grade 5 spelling), but its inclusion does not alter the size and significance of the main results.

Table 9 adds school fixed effects to the regression. With this we control for the possibility that time-invariant factors at the school level are driving our results. This could be the case, for example, when students are strongly sorted into schools according to their background characteristics. In this case the association between student characteristics and learning gains could be driven by (unobserved) differences between schools that house different kinds of student populations. The results from Table 9 however show that including school fixed effects does not change the patterns of the found associations.

Finally, Table 10 shows the results of our main specification as well as the previously discussed robustness checks for the pooled sample over all grades. This table further demonstrates that while there are some slight differences in terms of significance between specifications when looking at grades separately, the overall picture of the disparate impact of COVID-19 on student learning gains along household income and parental education levels remains strong and is robust to various alternative model specifications.

Trends in learning gain over time

The interpretation of the differences in learning gains between the cohort affected by the COVID-19 induced lockdown and previous cohorts as attributable to the impact of COVID-19 hinges on the assumption that learning gains would have been similar in the absence of the pandemic. While this assumption is untestable, we can provide supporting evidence for it by looking at the variability of learning gains for all grades over time. If these trends are relatively stable, we can be reasonably sure that the difference between the 2019/2020 cohort and the previous cohorts was caused by the impact of the pandemic. Figs 5- 7 show the trends in learning gains per grade over time by plotting the (inverse probability population weighted) unstandardized learning gains for all available cohorts in reading, spelling, and math respectively. Because our data does not go back equally far for all grades, the lines are of different length. As noted earlier, we do not have information on grade 1 learning gains for the reading domain, as grade 1 students do not take a midterm test for this domain. For higher grades, we also have fewer available cohorts due to the manner in which data was collected (see also Table 1). Hence, for grade 2 we have data going back to academic year 2014/2015, for grade 3 from 2015/2016 onwards, for grade 4 starting in 2016/2017, and grade 5 starting 2017/2018.

The figures clearly show a marked decrease in learning gains between the COVID-19 affected cohort of the school year 2019/2020 relative to the prior cohorts in all domains and for most grades. For the spelling and math domains, learning gains prior to the COVID-19 cohorts were remarkably stable over time across grades 1 through 4. For grade 5, the 2018/2019 cohort had somewhat lower learning gains than the 2017/2018 cohort. However, since these are the only 2 pre-COVID-19 cohorts for which grade 5 data is available, it is unclear whether this represents a somewhat random fluctuation between cohorts, or whether it is part of a longer trend in declining grade 5 learning gain. For reading, the results are less clear. Grades 2 and 3 show a less stable trend over time than the other grades. For our main estimation sample of the 2017 and 2018 cohorts comprising the control group however, the differences in learning gain between these two cohorts is relatively small for all grades.

A different way of showing whether the 2019/2020 COVID-19 affected cohort is somewhat of an outlier in terms of their regular learning gain, is to plot the prior performance of this cohort during the years in which there was no pandemic and compare it to the performance of an earlier cohort. Figs 8- 10 show the learning gain in all prior grades of the students who were in grade 5 during the 2019/2020 school year and those who were in grade 5 during the 2018/2019 school year for reading, spelling and math respectively. If the 2019/2020 cohort is relatively similar to the prior cohort, we should expect to see similar levels (and trends) of learning gain for grades 1 through 4. The pandemic occurred during grade 5 for the 2019/2020 cohort, and we should therefore expect lower levels of learning gain in grade 5 for the 2019/2020 cohort compared to the students that were in grade 5 during the 2018/2019 school year.

Looking at the figures, this is indeed what we see for spelling and math. Both cohorts are on remarkably similar learning gain trajectories from grade 1 through grade 4. In grade 5, the 2019/2020 cohort experiences a stronger decline in learning gain, especially in math, than the students of the previous cohort that were unaffected by the pandemic. For reading, the results are again less stable. The 2018/2019 cohort experienced a stronger decline in learning gain in grade 3 relative to the other grades and the 2019/2020 cohort. The overall pattern of decreasing learning gain from grade 2 to grade 3 and increasing learning gain from grade 3 to grade 4 is visible for both cohorts, however, and the grade 5 learning gain of the pandemic-affected 2019/2020 cohort does decrease more strongly than the learning gain of the prior, unaffected cohort.

Conclusions

This study describes the additional inequality in learning gains of primary school students in the Netherlands during 12 weeks of disrupted learning due to the COVID-19-pandemic for three domains: reading, spelling and math. We show large inequalities in the learning loss based on parental education and parental income, on top of already existing inequalities. The additional inequality in learning gains is largest among grades 2 and 3.

These results are quite alarming and indicate an average delay in learning of about 5.5 weeks for reading, and around 3 weeks for spelling and math with larger deficits in the higher grades. Relative to the period between the midterm- and end-of-term tests of around 20 weeks, this is rather a lot. It is to some extent reassuring that in general the decline in learning gains do not take place in the lowest grades in which the foundation for math and language skills are laid [ 34]. On the other hand, the decline in learning gains is larger for students from a low socioeconomic background (lower parental education and household income) for spelling an math and these inequalities are higher in the early grades. We see a delay of around 4 weeks for spelling and math for students with low-educated parents. This implies that during the school closure period students with low-educated parents hardly learned anything. However, there are no statistically significant socioeconomic status differences in reading scores. Previous research has shown that the home environment is important for the development of literacy skills and reading motivation [ 35, 36]. In line with this finding some have also suggested that center-based reading interventions might be less effective than mathematics interventions [ 37, 38]. Our finding that reading skills are less affected by the school closure support the idea that the family environment plays an important role in the development of reading skills, also when schools are open. In contrast, the limited increase in socioeconomic inequalities in reading skills is not in line with our expectations. Normally, family environment is an important source of inequality in reading skills [ 36] and we would expect inequalities to rise when schools closed and the role of family environment increased. We attribute the additional inequality in learning loss of students in math and spelling based on parental education and income to better resources these students had: Students with higher-educated parents most likely all possessed a laptop, had parents that were able and willing to help with schoolwork and could even afford additional private tutoring if needed.

The results call for national focus on reducing the learning loss of students from lower-educated parents and lower household income. It is worrisome and unfortunately not unlikely that the increased inequalities in learning loss due to the pandemic may lead to long lasting inequalities, deepening the gap in adult outcomes between groups in the population. This very much stresses the need for targeted interventions to reduce the current inequalities in learning loss caused by the pandemic.

This article shows that schools matter, specifically for the most vulnerable groups. Distance learning may prevent part of the damage but cannot compensate for classroom teaching. The policy implications of these findings are therefore twofold: 1) Government budgets that are made available to make up for learning loss should give schools with many students from low-educated parents and low household income a large share of the pie, and 2) in an event of another crisis, or the current COVID-19-pandemic continues, schools should be closed only as a very last resort to avoid further inequalities.

References

1. 6. Kuhfeld M, Tarasawa B, Johnson A, Ruzek E, Lewis K. Learning during COVID-19: Initial findings on students' reading and math achievement and growth. NWEA Brief, Portland, OR. 2020.

2. 8. Rose ST, L. Twist; Lord, P.; Rutt, S.; Badr, K.;, Hope, C.; Styles, B. Impact of school closures and subsequent support strategies on attainment and socio-emotional wellbeing in Key Stage 1. London: 2021 Contract No.: Interim Paper 1.

3. 9. Contini D, Di Tommaso ML, Muratori C, Piazzalunga D, Schiavon L. The COVID-19 Pandemic and School Closure: Learning Loss in Mathematics in Primary Education. Institute of Labor Economics (IZA), 2021 Contract No.: 14785.

4. 13. Domingue BW, Hough HJ, Lang D, Yeatman J. Changing Patterns of Growth in Oral Reading Fluency during the COVID-19 Pandemic. Working Paper. Policy Analysis for California Education, PACE. 2021.

5. 30. Commission E. The Digital Economy and Society Index (DESI). Thematic chapter. 2020.

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Sharp increase in inequality in education in times of the COVID-19-pandemic

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The COVID-19-pandemic forced many countries to close schools abruptly in the spring of 2020. These school closures and the subsequent period of distance learning has led to concerns about increasing inequality in education, as children from lower-educated and poorer families have less access to (additional) resources at home.

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The article emphasizes that these disparities are not just about the lack of technology; they also reflect systemic inequalities that have been perpetuated for years. The ethical implications of these findings are significant: by not providing equal access to digital education tools, society is contributing to deeper inequality in education and economic opportunity.

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 The Digital Divide in Education: A Major Barrier to SDG 4

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 The Digital Divide in Education: A Major Barrier to SDG 4

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The digital divide is not just a technological issue—it is a social and economic issue that hinders access to education and perpetuates inequality. SDG 4, which focuses on ensuring inclusive and equitable quality education for all, faces significant challenges due to this divide. As the world becomes more digitized, access to the internet and digital devices has become an essential tool for learning, and those who lack these tools are at a disadvantage.

In rural and remote communities, as well as in many developing countries, lack of infrastructure such as reliable electricity and internet connectivity further exacerbates the digital divide. According to a report by the International Telecommunication Union (ITU), approximately three billion people worldwide do not have access to the internet, and most of these people are from developing countries or rural areas. Without reliable internet access, students cannot participate in online education, leaving them unable to keep up with their peers who have access to digital resources.

In developed countries, even though internet infrastructure may be more readily available, the issue of affordability remains a significant concern. Students from low-income households may have access to the internet, but they often do not have the devices necessary for online learning or face challenges in getting their families to support their digital education efforts. Stelitano et al. (2020) found that, in some areas of the United States, 30% of students from low-income households lacked access to a reliable internet connection, compared to just 3% in wealthier areas.

This disparity in access to digital tools means that the most vulnerable students are not only deprived of educational opportunities, but they also face longer-term consequences in terms of career prospects and economic mobility. Without the skills necessary to participate in the digital economy, these students are left behind.

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 The Role of Governments and Corporations in Addressing the Digital Divide

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 The Role of Governments and Corporations in Addressing the Digital Divide

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Both governments and corporations play a critical role in bridging the digital divide. Governments must step in to provide the necessary infrastructure, such as high-speed broadband, to underserved regions. They can also subsidize digital tools and devices for students in low-income communities. Meanwhile, corporations, particularly those in the technology sector, can support initiatives aimed at closing the digital gap through corporate social responsibility (CSR) programs.

For example, technology companies like Google and Microsoft have already made strides in providing technology and support to underserved schools. Microsoft’s "TechSpark" initiative aims to create digital skills programs and provide technology access to underserved areas in the U.S. through strategic partnerships. These efforts are a good starting point, but much more needs to be done to ensure that all students, particularly those from marginalized backgrounds, have the resources they need to succeed in the digital world.

Furthermore, governments need to invest in digital literacy programs, not just for students but also for teachers and parents. A study by OECD (2020) found that digital literacy was a key factor in determining how effectively students could participate in online learning. Without sufficient training for teachers and parents, even students with access to technology might not get the most out of it.

# data[].attributes.headline
 Increase Investment in Digital Infrastructure

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 Increase Investment in Digital Infrastructure

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Governments must invest in building the digital infrastructure required for equitable access to education. This includes providing high-speed internet in rural and underserved areas and ensuring that schools have access to digital learning platforms. Public-private partnerships can help achieve these goals, with technology companies providing equipment and training.

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 Provide Affordable or Free Digital Devices

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 Provide Affordable or Free Digital Devices

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Governments should introduce programs that offer subsidized or free digital devices to students from low-income households. This would ensure that students in underserved communities are not left behind in the shift to digital learning. Programs could include laptop loans, tablet subsidies, or partnerships with tech companies to provide discounted or free technology.

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Digital Literacy Programs for Students and Teachers

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Digital Literacy Programs for Students and Teachers

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A comprehensive digital literacy curriculum should be integrated into educational systems worldwide, ensuring that both students and teachers have the skills necessary to navigate digital platforms and online learning environments. Additionally, parents should be involved in these programs to support students at home.

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Technology companies should take responsibility for closing the digital divide through corporate social responsibility (CSR) initiatives. Companies can provide low-cost devices, internet access, and training programs for underserved communities. Furthermore, corporations can partner with governments and NGOs to create community-based solutions that address the digital education gap.

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Achieving SDG 4—Quality Education—requires skilled professionals across a variety of fields. Many career paths are directly related to improving access to education, bridging the digital divide, and ensuring equity in education for all. Below are some of the key roles that support the goal of providing inclusive, equitable quality education:

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Job Description:
A Policy Analyst in the education sector works to understand and analyze how education policies impact access to technology and digital learning tools. They play a crucial role in advising government bodies and education departments on the best ways to integrate digital learning into national or regional education strategies. They also help identify policies that need to be reformed in order to better serve disadvantaged students.

Responsibilities:

Research and analyze existing education policies regarding digital inclusion and access to technology.

Work with government officials and educational institutions to advocate for policies that ensure equitable access to digital education resources.

Evaluate the impact of current education policies on marginalized communities, particularly in terms of digital education.

Provide recommendations for policy changes to improve digital access and inclusion.

Skills Needed:

Strong research and data analysis skills.

Experience in policy writing and advocacy.

Understanding of educational systems, especially related to digital education.

Ability to work with stakeholders in both the public and private sectors.

Example: A policy analyst might work for a government education department to evaluate the effectiveness of a national policy aimed at providing digital devices to public schools and recommend adjustments to ensure equitable access for all students.

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Job Description:
A Digital Education Program Manager oversees the planning, implementation, and evaluation of programs designed to promote digital learning in schools, universities, and community organizations. This role involves managing the budget, resources, and team responsible for executing digital education initiatives. It is a key role in ensuring that SDG 4 is achieved by providing students and educators with the tools and training they need to succeed in the digital world.

Responsibilities:

Develop and manage digital education programs aimed at improving learning outcomes.

Oversee the delivery of training programs for teachers on how to integrate technology into their classrooms.

Manage partnerships with tech companies, NGOs, and government agencies to secure resources for digital education initiatives.

Monitor and evaluate the success of digital programs and make adjustments as necessary.

Skills Needed:

Strong project management and team leadership skills.

Knowledge of digital learning tools and how to integrate them into education systems.

Experience with budgeting and resource management.

Ability to evaluate program effectiveness and make data-driven decisions.

Example: A Digital Education Program Manager might work for an international NGO to provide online education and digital resources to refugees or displaced communities. They would oversee the creation of digital classrooms and the development of online curricula to reach students who have no access to traditional schools.

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Education: From COVID-19 school closures to recovery

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Education: From COVID-19 school closures to recovery

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After the historic disruption of the COVID-19 pandemic, most schools are back open worldwide but education is still in recovery assessing the damage done and lessons learned. Education: The pandemic affected more than 1.6 billion students and youth globally, with the most vulnerable learners being hit hardest. Some gains already made towards the goals of the 2030 Education Agenda were lost.