Disruptions to Schooling and the Need for Recovery

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Silvia Montoya, Director UNESCO Institute for Statistics and Martin Gustafsson, 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:

  1. In many developing countries the percentage of children considered proficient was increasing by two percentage points a year before COVID-19
  2. COVID-19 school disruptions have caused learning losses equal to all of the learning gains in the last two decades
  3. 101 million children and youth from Grades 1 to 8 will fall below the minimum proficiency level after 2020 [note]
  4. For each month of no or little contact between teacher and learner two months of learning were lost
  5. Recovery could occur by 2024, but only if exceptional efforts are devoted to the task through remedial and catch-up strategies

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Reconstruire en mieux après la COVID-19 : l’importance du suivi des inégalités d’apprentissage

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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)

De notre choix de mesure dépend notre compréhension de la taille et de la nature du problème. Dans un billet de blog récent, nous expliquions pourquoi la mesure de la pauvreté des apprentissages convient bien au suivi des impacts de la COVID-19. Dans ce billet, nous nous pencherons sur deux concepts complémentaires : l’écart de la pauvreté des apprentissages et la sévérité de la pauvreté des apprentissages, afin d’examiner la distribution des pauvres en apprentissage et de mesurer l’incidence de son évolution sur les inégalités d’apprentissage. L’ISU fournit des données sur ces deux concepts. Les données de nombreux indicateurs clés de l’ODD 4 ont été actualisées à l’occasion du rafraichissement des données de mars. L’ISU recueille aussi les données relatives aux réponses nationales à la COVID-19 sur l’éducation, équité et inclusion.

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Una mejor reconstrucción tras la COVID-19: La importancia de hacer un seguimiento de la desigualdad en el aprendizaje

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João Pedro Azevedo, economista jefe, Education Global Practice, Grupo Banco Mundial y Silvia Montoya, Directora, Instituto de Estadística de la UNESCO (UIS)

La elección de un indicador determina nuestra comprensión del alcance y la naturaleza de un problema. En un blog reciente, explicamos por qué la medición de la pobreza en el aprendizaje es adecuada para monitorear el impacto de la COVID-19 en la educación. En este blog, analizamos dos conceptos adicionales: la brecha de la pobreza en el aprendizaje y la gravedad de la pobreza en el aprendizaje, para conocer la distribución de los afectados por la pobreza en el aprendizaje y medir cómo afectan estos cambios a la desigualdad en el aprendizaje. El UIS proporciona datos para ambos conceptos, así como para muchos indicadores clave del ODS 4. La última actualización de datos se produjo en marzo. El UIS también está recopilando información sobre la respuesta nacional frente a la COVID-19 en educación, equidad e inclusión.

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Building Back Better After COVID-19: The Importance of Tracking Learning Inequality

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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.

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Cómo el marco del ODS 4.1.1 y la pobreza en el aprendizaje pueden ayudar a los países a dirigir su respuesta a la COVID-19 en materia de política educativa

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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 del ODS 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.

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Comment le cadre de l’ODD 4.1.1 et le concept de pauvreté des apprentissages peuvent-ils aider les pays à orienter leur politique d’éducation en réponse à la COVID-19

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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.

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How the SDG 4.1.1 Framework and Learning Poverty Can Help Countries Focus Their Education Policy Response to COVID-19

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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.

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Bridging CESA 16-25 and SDG 4: Using Regional Benchmarks to Meet Education Objectives

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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.

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Using Household Surveys to Achieve the SDG 4 Objectives of Inclusivity and Equity

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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.

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New INEE Reference Group to Drive Reforms and Set Global Standards for EiE Data

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By ECW, FHI 360, INEE, NORRAG, and the UIS

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. 

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