Learning what our children have learnt during the pandemic

Group Submission Type: Formal Panel Session

Proposal

The COVID-19 pandemic has caused unprecedented global disruptions in education. Over 1.6 billion students were affected by school closures as the crisis paralysed education systems around the world. Although almost every country in the world provided students with remote learning options, the effectiveness and scope of such initiatives varied widely, and they were, at best, a partial substitute for in-person instruction. In low- and middle-income countries, school closures often lasted longer than in high-income countries, and the response to school closures was typically less effective.
Even before the pandemic, almost 60% of all 10-year-olds in middle and low-income countries couldn’t read or understand a simple story. The pandemic has only exacerbated this learning crisis and children in almost every country have fallen behind in their learning. A recent report has estimated that learning poverty has increased by a third in low- and middle-income countries, with an estimated 70% of 10-year-olds unable to understand a simple written text.
Lack of learning data is being cited as a crucial obstacle for tackling the current widespread learning crisis. COVID-19 increased the need for frequent, timely, actionable, and comparable data to assess the impact of school closures on students’ learning. Yet in many low- and middle-income countries, learning data are not collected frequently, or in worst cases, at all. Even when learning-related data are collected, the comparability and validity of such data and the capacity to analyse, understand and use it for decision making remains a significant challenge. Without regular and reliable data to measure learning outcomes, countries cannot monitor change in learning levels and whether their investments and policies are working for all children. Efforts are also needed to ensure that learning data can be disaggregated by sex, age, location, and other relevant characteristics to offer actionable insights for targeted learning improvement programs.
Existing actual and simulated evidence shows that the size of learning losses varies across grades, subjects, social and economic groups, and effectiveness of the education system. Measuring learning loss would allow for understanding which ages and grades, subjects and competencies and socio-economic groups were affected the most by school closures and might require greater attention. Collecting learning loss data will also create the baseline upon which recovery efforts can build and be monitored against. Evidence on recent policy interventions and their implementation is also needed to better understand their effectiveness across different settings and their potential to mitigate learning loss amidst the possibility of similar shocks in the future.
To adequately measure learning loss, data needs to be collected at two or more points in time; one prior to the outbreak of COVID-19 (a baseline measure) and one after the disruption to determine any change over time. So far, most studies and discussions on learning loss during the pandemic have relied on simulations, retrospective data or smaller non-representative data collections. This panel spotlights assessment instruments and programs, methods and large-scale evidence on learning loss allowing direct comparison of student learning outcomes before the pandemic to performance post pandemic following the period of disruption.
The first paper describes ASER’s citizen-led model for large-scale assessments and describes its relevance to measure foundational literacy and numeracy learning outcomes, especially in low resource settings. The paper tracks the changes in foundational learning outcomes across several states in rural India since 2018 to understand the effects of the pandemic on children's foundational learning. Learning outcomes are disaggregated by grade to highlight where the attention of the learning improvement programs should focus.
The second paper presents data from the International Common Assessment of Numeracy (ICAN), a large-scale assessment implemented by the members of the People’s Action for Learning (PAL) Network. ICAN adapted several features of existing ASER-like CLAs. The first implementation round of ICAN was completed in one district each of 13 countries across Africa, the Americas and Asia in 2019 surveying around 20,000 children. Data from this round showed that a large proportion of children in grades 2 and 3 were not able to do numeracy tasks aligned to the minimum proficiency level descriptor for SDG 4.1.1 (a), thus emphasizing the need for urgent and targeted interventions beginning earlier in the learning cycle of young children. In 2022, the ICAN assessment is being repeated in 7 countries across Africa and Asia. Using the data from these two rounds of ICAN’s multi-country implementation, this paper presents trends in learning in selected countries, trying to understand the status of foundational learning after Covid-19-related school closures and reopenings.
The third paper spotlights findings from the COVID-19 Monitoring Impacts on Learning Outcomes (MILO) project. The MILO project was designed to provide information on the impact of the pandemic on learning outcomes in six countries in Africa – Burkina Faso, Burundi, Côte d’Ivoire, Kenya, Senegal and Zambia. The main aim of the MILO study was to determine the impact of COVID-19 on learning outcomes at the end of primary schooling. Data on learning outcomes prior to the pandemic were available through the national or regional assessments (historical assessments) that had been administered by the six countries in 2019, or 2016 in the case of Zambia. The historical assessments were re-administered in the six MILO countries in 2021. These assessment data provided a comparison against assessment data from previous years. The performance for the target population in 2021 was compared against an equivalent cohort prior to the outbreak of the COVID- 19 pandemic. Assessments for Minimum Proficiency Levels for SDG 4.1.1b (AMPL-b) tests were developed in the MILO project to provide a measure against SDG 4.1.1b.
Overall, the first three papers in the panel presents large-scale assessment evidence on change in learning levels prior to the pandemic and post pandemic from multiple contexts around the world. The panel aims to raise important questions around assumptions regarding children’s learning loss, especially in the primary grades. Finally, the fourth paper in this panel elaborates a conceptual framework for understanding drivers of education system performance to addressing the learning crisis after the COVID-19 pandemic.

Sub Unit

• General Pool

Chair

• Ramya Vivekanandan, Global Partnership for Education

Individual Presentations

• Every child counts: a common learning assessment exploring learning loss in numeracy in the Global South - Nicolas Buchbinder, PAL Network

• Using global benchmarks to examine the impact of COVID-19 on learning - Ursula Schwantner, Australian Council for Educational Research, Global Education Monitoring (GEM) Centre

• Purpose driven education systems - Michelle Kaffenberger, RISE Programme, University of Oxford