Silvia Montoya, Director UNESCO Institute for Statistics and Martin Gustafssson, Research on Socio-Economic Policy (ReSEP), University of Stellenbosch
The 2020 learning losses equal the gains made in the last 20 years
Some numbers to retain:
In many developing countries the percentage of children considered proficient was increasing by two percentage points a year before COVID-19
COVID-19 school disruptions have caused learning losses equal to all of the learning gains in the last two decades
101 million children and youth from Grades 1 to 8 will fall below the minimum proficiency level after 2020 [note]
For each month of no or little contact between teacher and learner two months of learning were lost
Recovery could occur by 2024, but only if exceptional efforts are devoted to the task through remedial and catch-up strategies
The effects of the pandemic on children have been multifaceted and for too many, devastating. Before the pandemic, the data suggested that learning proficiency was gradually improving, though not fast enough to reach the Sustainable Development Goal 4 (SDG 4) objective of all children and young people being minimally proficient in reading and mathematics by 2030. Yet was progress was occurring. A new report by the UNESCO Institute for Statistics (UIS) examines the learning losses associated with school closures and considers how interventions may help children catch up.
In many developing countries the percentage of children considered proficient was increasing by two percentage points a year[1]. To provide an idea of the global trend, among the 130 million children of the age corresponding to Grade 3, only 73 million could read proficiently before the pandemic, but this was increasing by 700,000 a year. Each year roughly 700,000 additional children were proficient, compared to the previous year[2]. And this is counting just one school grade. This was mainly because learning improved in schools, but to some extent better school participation rates played a role.
Recovering from COVID-19 learning losses will depend on catch-up strategies
The pandemic brought with it school closures of a scale never previously seen. At the most serious point, around March and April 2020, around 95% of children were not attending school. It has been estimated that by early November, the world’s learners had lost between 41% and 68% of the contact schooling they should have received in 2020. It has been difficult to establish precise figures mainly because once schools re-opened many only allowed learners to attend on a rotational basis[3].
João Pedro Azevedo, Lead Economist, Education Global Practice, World Bank Group and Silvia Montoya, Director, UNESCO Institute for Statistics (UIS)
Our choice of a measure shapes our understanding of the size and nature of a problem. In a recent blog we discuss why the learning poverty measure is well suited to monitor the educational impacts of COVID-19. In this blog, we discuss two complementary concepts: the learning poverty gap and learning poverty severity, to look at distributions among the learning poor and measure how these changes affect learning inequality. The UNESCO Institute for Statistics (UIS) and the World Bank provide data for both of these concepts. Data for many key SDG 4 indicators were updated in the March data refresh. The UIS is also collecting data on the national response to COVID-19 on education, equity and inclusion.
Understanding changes in learning inequality through the learning poverty gap, learning poverty severity and minimum proficiency
While learning poverty is a simple concept to grasp, this indicator alone does not provide a picture of the learning level and distribution of learning among those below the minimum proficiency level (MPL). Because learning poverty is a headcount ratio, estimates treat all students below the minimum proficiency level as being equally learning deprived. It also does not reflect improvements in learning below the MPL threshold, which can fail to create compatible incentives as it may miss progress in foundational subskills critical for developing reading proficiency, for example, knowledge of spoken words and how to use them, hearing and making the sounds of words, mapping sounds to letters and letters to sounds while learning letter names, among others, as described in the reading rainbow (Figure 1). Understanding the heterogeneity among the learning poor is critical to combat learning poverty as children who do not master these subskills in early primary grades remain unable to read with comprehension.
Figure 1: SDG 4.1 framework and reading rainbow of literacy subskills
Figure 2: Distributional sensitive measure of learning deprivation
João Pedro Azevedo, economista jefe, Education Global Practice, Grupo Banco Mundial y Silvia Montoya, Directora, Instituto de Estadística de la UNESCO (UIS)
La mayoría de los gobiernos y asociados para el desarrollo están trabajando en la identificación, protección y apoyo del aprendizaje de los miembros más vulnerables de la generación COVID-19. En este blog, analizamos cómo el marco delODS 4.1.1 y el concepto de pobreza en el aprendizaje son herramientas útiles para ayudar a los países a entender y corregir los efectos de la COVID-19 en la escolarización y el aprendizaje.
Del nivel mínimo de competencias a una medición de la privación del aprendizaje
En octubre de 2018, la comunidad internacional acordó ser prudente en la utilización de un estándar global para el seguimiento del progreso en el aprendizaje de los estudiantes. El nivel mínimo de competencias (NMC) acordado a través de la Alianza Global para el Seguimiento del Aprendizaje (AGSA) ofrece un punto de referencia único para ayudar a los países y los asociados para el desarrollo a trabajar juntos para monitorear y mejorar el aprendizaje de aquellos estudiantes que se están quedando atrás. La pantalla de visualización interactiva que se muestra a continuación (Figura 1) permite explorar con el control deslizante los datos utilizados para monitorear este ODS, utilizando tanto el NMC de la AGSA como diferentes niveles mínimos de competencia.
Figura 1. Ejemplo de cómo el ODS 4.1.1 puede utilizarse para poner el foco en los estudiantes que están por debajo del nivel mínimo de competencias (NMC)
La pobreza en el aprendizaje: un indicador multidimensional para el sector educativo
En octubre de 2019, el Banco Mundial y el Instituto de Estadística de la UNESCO (UIS) lanzaron un nuevo indicador multidimensional llamado pobreza en el aprendizaje. Se basa en la idea de que todos los niños deberían estar escolarizados y ser capaces de leer un texto apropiado para su edad a los 10 años[1]. Esta formulación refleja cuál es su aspiración y sirve como indicador de alerta temprana del Objetivo de Desarrollo Sostenible 4 (ODS 4), de que todos los niños deben estar escolarizados y aprendiendo[2], y se basa en dos privaciones.
João Pedro Azevedo, économiste principal, Pôle mondial d’expertise en éducation, Groupe de la Banque mondiale et Silvia Montoya, directrice, Institut de statistique de l’UNESCO (ISU)
La plupart des gouvernements et des partenaires de développement s’emploient à connaître, à protéger et à soutenir l’apprentissage des membres les plus vulnérables de la génération COVID-19. Dans ce blog, nous examinons de quelle manière le cadre de l’ODD 4.1.1 et le concept de pauvreté des apprentissages sont en mesure d’aider les pays à comprendre les impacts de la COVID-19 sur la scolarité et l’apprentissage, et à prendre les mesures appropriées pour les atténuer.
Du seuil minimal de compétences à la mesure de la pauvreté des apprentissages
En octobre 2018, la communauté internationale a convenu de réfléchir sur le suivi des progrès de l’apprentissage des élèves à l’aide d’une norme mondiale. Le Seuil Minimal de Compétences (SMC), approuvé par l’Alliance mondiale pour la mesure de l’apprentissage, fournit une valeur de référence incomparable pour aider les pays et les partenaires de développement à travailler de concert pour suivre et pour améliorer l’apprentissage des élèves qui ont du retard. La visualisation interactive ci-dessous (figure 1) vous permet d’explorer les données utilisées pour le suivi de cet ODD à l’aide du SMC-GAML et des différents seuils minimaux de compétence en déplaçant le curseur.
La figure 1 montre comment utiliser l’ODD 4.1.1 pour mettre l’accent sur les élèves en dessous du Seuil Minimal de Compétences (SMC).
La pauvreté des apprentissages : un facteur multidimensionnel pour le secteur éducatif
En octobre 2019, la Banque mondiale et l’Institut de statistique de l’UNESCO (ISU) ont lancé le nouvel indicateur multidimensionnel appelé « pauvreté des apprentissages ». Il est fondé sur la notion selon laquelle chaque enfant devrait aller à l’école et savoir lire un texte adapté à son âge à 10 ans.[1] Cette formulation, qui traduit l’aspiration de l’Objectif de Développement Durable (ODD) 4 qui stipule que tous les enfants doivent aller à l’école et apprendre[2] et qui tient lieu d’indicateur d’alerte précoce, repose sur deux privations.
João Pedro Azevedo, Lead Economist, Education Global Practice, World Bank Group and Silvia Montoya, Director, UNESCO Institute for Statistics (UIS)
Most governments and development partners are working on identifying, protecting, and supporting learning of the most vulnerable members of the COVID-19 generation. In this blog, we examine how the SDG 4.1.1 framework and the concept of learning poverty are well positioned to help countries understand and act on the impacts of COVID-19 on schooling and learning.
From the minimum proficiency level to a measure of learning deprivation
In October 2018, the international community agreed to be deliberate about tracking progress in learning of students using a global standard. The minimum proficiency level (MPL) agreed through the Global Alliance to Monitor Learning offers a unique benchmark to help countries and development partners work together to monitor and improve learning for these students that are falling behind. The interactive visualization linked to in the image below (Figure 1) allows you to explore the data used to monitor this SDG, using both the GAML MPL as well as different minimum proficiency levels by interacting with the slider.
Figure 1 shows how SDG 4.1.1. can be used to generate focus on students below the minimum proficiency level (MPL)
Learning poverty: a multidimensional indicator for the education sector
In October 2019, the World Bank and the UNESCO Institute for Statistics (UIS) launched a new multidimensional indicator called learning poverty. It is based on the notion that every child should be in school and be able to read an age-appropriate text by age 10.[1] This formulation reflects the aspiration and serves as an early warning indicator of Sustainable Development Goal (SDG) 4 that all children must be in school and learning[2] and builds on two deprivations.
Learning poverty (and Indicator 4.1.1 on learning deprivation) has many desirable characteristics, including simplicity and focus on those in the bottom of the learning distribution (for a longer discussion on some of the properties of the learning poverty measure please see a recent paper). It brings together schooling and learning indicators, as it combines the share of primary-aged children out-of-school who are schooling deprived (SD), and the share of pupils below a minimum proficiency in reading, who are learning deprived (LD). This measure implies that both “more schooling”, which by itself serves a variety of critical societal functions, as well as “better learning” which is important to ensure that time spent in school translates into acquisition of skills and capabilities.
Figure 2 provides an animation which numerically and visually illustrates the concept of learning poverty
The learning poverty indicator is calculated as follows:
LP = [LD x (1-SD)] + [1 x SD]
LP = Learning poverty
LD = Learning deprivation, defined as share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring
SD = Schooling deprivation, defined as the share of primary aged children who are out-of-school. All out-of-school children are assumed to be below the minimum proficiency level in reading.
By construction, learning poverty can be affected by changes in its two dimensions: (i) learning deprivation as the share of students below the minimum proficiency threshold improves or worsens, or (ii) schooling deprivation as access or age-grade distortion changes due to shocks or policies.
While schooling deprivation can be directly observed depending on whether the child is enrolled or not enrolled in school, learning deprivation cannot be directly observed, and is measured through standardized learning assessments using SDG’s definition of minimum proficiency level, where reading proficiency is defined as:
“Students independently and fluently read simple, short narrative and expository texts. They locate explicitly stated information. They interpret and give some explanations about the key ideas in these texts. They provide simple, personal opinions or judgements about the information, events and characters in a text.” (UIS and GAML 2019)
SDG 4.1.1, learning poverty and COVID-19
The SDG 4.1.1 framework and the learning poverty measure can help monitor and guide national conversations on the impacts and education policy response to COVID-19 by:
Reaching agreement and clarity on a minimum proficiency level: The GAML process, through the Global Proficiency Framework, has produced detailed documentation about the competences expected to be mastered at the minimum proficiency level (MPL). All this material can be used to inform a national conversation on what elements of the curriculum could be prioritized as the system reopens.
Focus on children falling behind: SDG 4.1.1 uses the MPL to measure the share of students above this threshold reflecting the aspiration that all children must be performing above the MPL. However, during a time of crisis and shock, such as COVID-19, countries might want to pay special attention to those students left behind. The latter is precisely what the measure of learning deprivation used in the learning poverty measure does.
Monitoring multiple dimensions of education: As schools close, students will lose learning. However, for certain sub-populations, COVID-19 might push students out of the educational system, increasing drop-out rates; and in certain countries, due to a choice of policies and practices, might increase repetition and age-grade distortion. Moreover, if school deprivation increases, through an increase of drop-out or of the age-grade distortion of previously low-performing students, it is statistically possible that average learning scores might increase or at least not fall as much after COVID-19. This misleading result can be avoided if effects are monitored using a measure which is simultaneously sensitive to changes in learning and access to schooling.
COVID-19 has led to an unprecedented crisis within an already existing global crisis of the education system in the developing world. The ability to use it as an opportunity to build back better will depend on the quality of our understanding of its effects. For that, both data and our choice of measures will be equally critical.
[2] SDG 4 makes this commitment: by 2030, signatories will “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.” The goal’s target 4.1 is to “ensure that all girls and boys complete free, equitable, and quality primary and secondary education leading to relevant and effective learning outcomes.”
By Silvia Montoya, Director of the UNESCO Institute for Statistics (UIS) and Daniel Capistrano, University College Dublin
This International Day of Education, the impact of COVID-19 on education is top of mind and finding solutions to revitalize learning is a priority, now more than ever. The UNESCO Institute for Statistics (UIS) is working with regional organizations and education partners to expand the global focus on benchmarking for the Sustainable Development Goals for education (SDG 4) so that regions and countries have more manageable, annual objectives.
With just a decade remaining to achieve SDG 4, it is imperative that all countries have the means to monitor progress and to plan necessary changes for the future. As the custodian of SDG 4 data and the lead agency providing internationally comparable and quality education data, the UIS has been working to help countries deal with this challenge.
One of the most effective ways of achieving the Agenda 2030 is by connecting existing efforts. The Africa Regional Report is a product of this collaborative strategy. Worldwide, there are several regional or sub-regional organizations that produce data and follow the progress of education policies based on common goals. Their transnational commitments require national and regional coordination and monitoring mechanisms to identify progress and obstacles. At the same time, they have articulated – or begun to articulate – their regional objectives with the Education 2030 Agenda.
By Friedrich Huebler, Head of Education Standards and Methodology at the UNESCO Institute for Statistics (UIS)
It is not enough to simply collect data. Data that are useful for monitoring progress towards Sustainable Development Goal 4 on education must be of high quality and comparable across countries. But collecting the data across a wide range of indicators has strained the data collection capacity of many Member States. At the same time, additional reporting needs brought on by COVID-19 have added further pressure to produce data as evidence for remedial action once schools fully re-open.
As the custodian agency for SDG 4, the UNESCO Institute for Statistics (UIS) works with countries to build their capacity to collect, produce and disseminate the data for monitoring progress towards international goals and for designing appropriate interventions, all while trying to mitigate the demands that this entails.
With this in mind, today the UIS is launching the 2020 SDG 4 Data Digest. This year, the Data Digest focuses on using household surveys to improve the scope of data collection while filling some of the gaps in administrative data.
To do this, the Data Digest explains the need for more and better data, serving as a “how-to” manual for ministries of education, national statistical offices and other education sector stakeholders. Readers will find information on everything from planning and design considerations for a household survey, to tips for writing compelling and effective questions, an interviewer’s check list of do’s and don’ts, the pros and cons of various modes of survey administration, along with implementation details like the most appropriate kind of field materials. The Data Digest also makes suggestions on how to communicate data findings.
In short, the 2020 SDG 4 Data Digest is the go-to source for a succinct overview of creating and implementing a household survey.
This post is cross-published by ECW, FHI 360, INEE, NORRAG, and the UNESCO Institute for Statistics.
Last week INEE, ECW, and the UIS launched a new Reference Group on education in emergencies (EiE) data aimed at tackling some of the sectoral challenges in EiE data collection, storage, sharing, and use. This new group fulfills part of the 2019 EiE Data Summit Action Agenda by enabling data experts from a range of organizations to collaborate on systemic EiE data issues that exist within and between their organizations.
In 2019 in Geneva, EiE data experts from almost 50 organizations participated in the EiE Data Summit to discuss and agree on ways forward on the following challenge: how, with limited resources and a growing number of crises, the EiE sector could collect more meaningful data and make new and existing data more accessible. More and better data enables better coordinated action, strengthens funding appeals, and informs monitoring and evaluation. Many of the challenges discussed – lack of incentives to share data, lack of standardized indicator definitions and methodologies, exclusion of marginalized groups – were identified as collective action issues that could not be solved by single institutions but instead require collaboration between a range of actors. The Summit’s Action Agenda therefore recommended the creation of an expert group to address some of these core issues.
Since then, the INEE Data and Evidence Collaborative – co-chaired by FHI 360 and NORRAG – has consulted a range of actors on how best to constitute this group before inviting ECW and the UIS to co-chair the INEE-convened group for the first year. Leadership from ECW and the UIS brings the best of both emergency and development context expertise to address increasingly prominent nexus issues.
Although there are a broad range of current education data initiatives globally, the consultation phase identified a specific gap in emergency contexts. This group does not intend to duplicate existing work but builds on and connects relevant initiatives within the group. As such, this group will replace a planned sub-group on emergency contexts for UNESCO’s Technical Cooperation Group on the Indicators for SDG 4.
By Andrés Sandoval-Hernández, University of Bath, and Diego Carrasco, Pontificia Universidad Católica de Chile
When UN Member States adopted the 2030 Agenda and its 17 Sustainable Development Goals (SDGs), there was not much discussion about how these goals were going to be measured. As we enter the Decade of Action, deciding on a measurement strategy for all SDGs and their targets has become a pressing issue.
We live in very challenging times. The rapid influx of immigrants, refugees and asylum seekers, along with increasing intolerance, social exclusion and feelings of alienation, extremism among young people, and the ongoing climate crisis, pose complex challenges. To face this global environment, we need information that enables us to think critically, connect our actions with their impacts, and act as empowered, active global citizens.
When looking specifically at SDG 4 for education, Target 4.7 asks Member States to “ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture’s contribution to sustainable development.”
Today, the 60th anniversary of the UNESCO Convention Against Discrimination in Education, we are reminded more than ever of the values of inclusion embodied in this target. While we say no to discrimination in education, let us say yes to inclusivity, respect for our differences and the right of all children to a quality education.
In this blog post we describe a recently developed strategy for assessing two indicators that embody tolerance, respect and sustainable development:
Indicator 4.7.4: Percentage of students by age group (or education level) showing adequate understanding of issues relating to global citizenship and sustainability.
Indicator 4.7.5: Percentage of 15-year-old students showing proficiency in knowledge of environmental science and geoscience.
Indicators 4.7.4 and 4.7.5 speak to empowering and enabling students to be active agents of positive change, while taking action to meet the other goals.
Using UNESCO Institute for Statistics (UIS) data, we are preparing an open-source, robust and easy-to-use document containing detailed technical guidelines for countries and other interested parties to collect the data necessary to produce the scales we discuss below.
Our measurement strategy is based on International Large-Scale Assessments (ILSAs) in education ((Sandoval-Hernández, Isac, & Miranda, 2019; Sandoval-Hernández & Carrasco, 2020)). In our view, ILSAs are a natural fit for assessing these particular thematic indicators because existing studies have already collected much of the relevant information. Our strategy includes a proposed conceptual framework, measurement models, a process to generate proficiency scores, and a method for establishing a threshold of ‘adequate’ and ‘satisfactory’ performance for Indicators 4.7.4 and 4.7.5, respectively.
We first identified a global content framework based on UIS data for defining and operationalizing Global Citizenship Education (GCED) and Education for Sustainable Development (ESD) (Hoskins, 2016; IBE, 2016). While there is no universal agreement on how to define or operationalize these concepts, it is possible to identify a set of guiding principles and themes.
We then carried out a mapping exercise to evaluate how to measure concepts in the framework using instruments and procedures of existing ILSAs. To do this, we identified the International Civic and Citizenship Study (ICCS) as the most valuable source of information for Indicator 4.7.4; and the Trends in Mathematics and Science Study (TIMSS) and the Programme for International Student Assessment (PISA) as the most informative for Indicator 4.7.5. These studies have the highest coverage of topics relevant to the SDGs, and a high potential to inform long-term monitoring. The results of this mapping exercise can be found here.
Producing scores
The analytical strategy to estimate the percentage of students who show ‘adequate’ and ‘satisfactory’ performance in each indicator had four main steps:
verifying the availability of observed responses to the items proposed by the mapping exercise,
testing the uni-dimensionality of the intended constructs,
fitting the corresponding measurement models to obtain scores for the categories and sub-categories of each indicator, and
establishing the cut-off points to identify ‘adequate’ or ‘satisfactory’ performance assuming a common measurement model.
A list of the categories and sub-categories of scores produced for Indicator 4.7.4, and a description of student knowledge at the cut-off points are shown in Figure 1.
Figure 1. Categories and descriptions for SDG 4.7.4
The percentage of students showing an ‘adequate’ understanding of issues related to global citizenship and sustainability (Indicator 4.7.4) – according to the scores and cut-off points – is shown in Figure 2. In this waffle plot, each square dot represents 1% of students reaching a certain standard (category or subcategory). As can be observed, there is an important variation in the proportion of students reaching the different standards both across countries and across standards. If we look at the cognitive standard, Latin American countries have lower proportions of students being able to make connections between processes related to global citizenship and sustainability and the legal and institutional mechanisms used to control them; while these proportions are generally higher in northern European and Asian countries (e.g. 19% and 32% in Dominican Republic and Peru vs 77% and 78% in Finland and Chinese Taipei).
Figure 2. Proportion of students reaching the SDG 4.7.4 standards in each country.
Note: Data is presented for all countries for which data is available in ICCS 2016. BFL= Belgium (Flemish); BGR= Bulgaria; CHL= Chile; TWN= Chinese Taipei; COL= Colombia; HRV= Croatia; DNK= Denmark; DOM= Dominican Republic; EST= Estonia; FIN= Finland; HKG= Hong Kong SAR; ITA= Italy; KOR= Korea, Republic of; LVA= Latvia; LTU= Lithuania; MLT= Malta; MEX= Mexico; NLD= Netherlands; DNW= North Rhine-Westphalia; NOR= Norway; PER= Peru; RUS= Russian Federation; SVN= Slovenia; SWE= Sweden.
As with global citizenship and sustainability, the results for Indicator 4.7.5 for environment science and geoscience, also show interesting variations both across countries and standards. A list of the categories and sub-categories for which scores were produce for Indicator 4.7.5 and a description of what students know or can do at the established cut-off points and are shown in Figure 3.
Figure 3. Categories and descriptions for SDG 4.7.5
The percentage of students showing ‘proficiency’ in environmental science and geoscience (Indicator 4.7.5) according to the scores and cut-off points is shown in Figure 4. As in Figure 2, each square dot represents 1% of students reaching a given standard.
When looking at the cognitive standard, Asian countries report the highest proportions of students who are able to apply and communicate concepts related to environmental science in everyday situations (e.g. Singapore, 59%). However, this pattern does not hold for the non-cognitive standards, where Botswanan and Kuwaiti students are the ones who report the highest enjoyment and confidence in learning science (51% and 39% respectively). Nevertheless, we insist that the real value of these measures is that given their reliability, relevance and timeliness, they can be used to inform the development of strategies to reach the targets included in Target 4.7. A full description of the thresholds used to set these standards and the items and methodology used to produce the respective scores can be consulted here.
Figure 4. Proportion of students reaching the SDG 4.7.5 standards in each country
Note: Data is presented for all countries for which data is available in TIMSS 2015. AAD= Abu Dhabi, UAE; ARM= Armenia; AUS= Australia; BHR= Bahrain; BWA= Botswana; ABA= Buenos Aires, Argentina; CAN= Canada; CHL= Chile; TWN= Chinese Taipei; ADU= Dubai, UAE; EGY= Egypt; ENG= England; GEO= Georgia; HKG= Hong Kong, SAR; HUN= Hungary; IRN= Iran, Islamic Rep. of; IRL= Ireland; ISR= Israel; ITA= Italy; JPN= Japan; JOR= Jordan; KAZ= Kazakhstan; KOR= Korea, Rep. of; KWT= Kuwait; LBN= Lebanon; LTU= Lithuania; MYS= Malaysia; MLT= Malta; MAR= Morocco; NZL= New Zealand; NOR= Norway; OMN= Oman; COT= Ontario, Canada; QAT= Qatar; CQU= Quebec, Canada; RUS= Russian Federation; SAU= Saudi Arabia; SGP= Singapore; SVN= Slovenia; ZAF= South Africa; SWE= Sweden; THA= Thailand; TUR= Turkey; ARE= United Arab Emirates; USA= United States.
Conclusions, limitations and suggestions for monitoring these indicators
We believe that studies like TIMSS, ICCS and PISA are well suited for providing at least a proxy measurement of Indicators 4.7.4 and 4.7.5. These ILSAs provide high coverage for the GCED and ESD themes, incorporate these topics naturally in their frameworks, collect comparable data consistently (allowing long-term monitoring), and have unrivalled data quality assurance mechanisms in place (ensuring data accuracy, validity and comparability).
It is, however, important to consider the limitations of this measurement strategy. For example, the data is confined to a specific level of education or student population (e.g. Grade 8 for ICCS and TIMSS; 15-year-old students for PISA). Another limitation is country coverage. The information available in the last cycles of TIMSS and ICCS allowed us to produce scores for 60 countries. While this is a significant number of countries, it is important to acknowledge that two-thirds of UN members do not participate in these studies.
Nevertheless, we believe that with a coordinated effort and support by all stakeholders, many more countries can collect the data for this measurement strategy so that we can work toward the elimination of discrimination in schools and create a more equal and just society.
By Silvia Montoya, Director, UNESCO Institute for Statistics
World Children’s Day, established to promote child rights and welfare, is more important than ever this year as the world grapples with dual threats to education and health. Creating the evidence for policy actions to mitigate the impact of school closures is crucial, and for this, countries must assess children’s learning, along with the effectiveness of remote schooling, while supporting families, teachers and other front-line workers. At the same time, keeping schools open is a priority, so taking measures to ensure children’s safety in school is central to preventing further closures during a second wave of COVID-19.
From the beginning of the pandemic, the UNESCO Institute for Statistics (UIS) has set the pace, collecting data on national government responses to the crisis, and collaborating with the World Bank and UNICEF on a joint report, What Have We Learnt. The report is based on two quarterly surveys – the first taking place between April and May with 118 respondents, and the second between July and October with 149 respondents.
The surveys confirm that students in low-income countries are most at risk from school closures. This is because lost school days and the perception (and perhaps reality) that learning from home does not have the same value as learning in the classroom, increases pressure on young people to drop out. Ultimately, only learning assessments will be able to tell us if remote schooling – online, TV and radio programming, as well as take-home paper-based work – have been effective. But in the meantime, 24 million students are at risk of dropping out this year, reducing their skills acquisition and earning prospects for years to come.
