Today, 650 million children around the globe are at risk of being left behind as they fail to learn basic skills. Inequitable access to education is part of the problem, but even when children are in school, they may not be learning. In Uganda, for instance, barely half of grade 6 children read at a grade 2 level. In India, just one in four children enrolled in grade 5 can read a simple sentence or complete simple division problems.
These challenges are widespread. According to the International Commission on Financing Global Education Opportunity (Education Commission), only one in ten children in low-income countries (four in ten in middle-income countries) are on track to gain basic secondary-level skills by 2030. Moreover, the obstacles to learning disproportionately affect marginalized populations—children in poor households or rural areas (especially girls), children with disabilities, and children affected by conflict and violence.
It is clear that the status quo is not good enough, but what should be done differently? While struggling schools would certainly benefit from better facilities and more teachers, research underscores that input-oriented solutions are likely insufficient. Many countries that dedicate substantial resources to education still fall short of ensuring that all children are learning. Meanwhile, relatively resource-poor education systems in Latvia and Vietnam, for example, punch above their weight in achieving greater gains for students than their peers with similar income levels.
Parents, teachers, policymakers, and school administrators need better tools to diagnose where and why learning gaps exist, and assess what strategies they can employ to turn things around. High-quality data and evidence are essential for both tasks.
Numerous governments, organizations, and companies have responded to this challenge and are generating copious amounts of data and analysis to support education decision-making around the world. Nonetheless, large gaps remain, as data management processes at the school and national level are often under-funded, ad hoc, and of variable quality and timeliness.
While continued investments in data creation and management are necessary, the ultimate value of information is not in its production, but its use. Herein lies one of the biggest challenges of translating information into actionable insights: those that produce education data are often far removed from those that make crucial decisions about education policies, programs, and investments. With limited insight on what decision-makers use and need, the likelihood of disconnect between supply and demand is high.
Yet, there has been surprisingly little systematic research on the types of information education decision-makers in developing countries value most—and why. Much of the available evidence on the use of education data in developing countries relies upon individual case studies. These qualitative snapshots offer deep insights on use patterns and challenges in a single context, but make it difficult to draw broader conclusions.
In this report, we offer a unique contribution to this body of knowledge by analyzing the results of two surveys of education policymakers in low- and middle-income countries that asked about their use of data in decision-making. Survey participants include senior- and mid-level government officials, in-country staff of development partner organizations, and domestic civil society leaders, among others. Respondents do not include local-level officials, school administrators, or teachers.
This report aims to help the global education community take stock of what information decision-makers use to measure results and manage change. We define information broadly, including raw statistical and administrative data, quantitative and qualitative analysis, learning assessments, and the results of program evaluations. Drawing upon our review of the literature and the two surveys of end users in developing countries, we offer practical recommendations to help those who fund and produce education data to be more responsive to what decision-makers want and need.
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