New Data Release: African Harmonised Early-Grade Assessment Dataset
Introducing AHEAD: A Harmonised Dataset for Early-Grade Learning Research Across Africa
Over the past two decades, Early Grade Reading Assessments (EGRA) and Early Grade Mathematics Assessments (EGMA) have generated an unprecedented wealth of data on foundational learning across Africa. Yet much of this evidence has remained underused—not only because access to datasets was often limited, but also because the data were difficult to use for comparative research.
Last year, the African Foundational Learning (AFLEARN) Data Hub contributed to the preservation and public release of more than 100 USAID-funded EGRA and EGMA projects through the DataLumos archive, ensuring that these valuable datasets remain available to researchers. But making data available is only the first step. Researchers still face substantial technical barriers: every assessment uses different variable names, coding systems, documentation, and data structures.
Data harmonisation addresses this second challenge by standardising comparable variables and integrating multiple datasets into a single resource while preserving links to the original studies.
Today, AFLEARN is pleased to announce the release of the African Harmonised Early-Grade Assessment Dataset (AHEAD). This is a long-term harmonisation programme designed to create a scalable, extensible resource for comparative research on foundational learning across Africa.
The current release of AHEAD brings together more than 130,000 learner records from 34 early-grade assessment surveys conducted across 14 African countries.
Why AHEAD Matters
Cross-country education research has traditionally required researchers to spend considerable time cleaning and restructuring datasets before meaningful analysis could begin. Even assessments measuring similar constructs often used different coding conventions, documentation, and variable structures.
AHEAD removes much of this technical barrier by providing common variable names and coding schemes across surveys. This makes it possible to compare learning outcomes across countries, investigate changes over time, examine contextual factors associated with learning, and synthesise evidence from multiple assessments within a common analytical framework.
Importantly, every harmonised variable retains traceability to its original project documentation, ensuring that analyses remain grounded in the context of each constituent study.
The AHEAD Data Catalogue
The first release of AHEAD includes not only the harmonised pupil-level microdata but also a comprehensive suite of supporting resources designed to promote transparency, accessibility, and reproducibility.
A key component of these resources is the AHEAD Data Catalogue, an interactive resource that provides comprehensive information on every assessment survey included in the harmonised dataset. The catalogue enables users to easily explore available surveys and provides detailed information on EGRA and EGMA subtasks, variable availability, known dataset limitations, and guidance on applying survey weights and accounting for complex sampling designs. It also includes direct links to the original constituent studies and their accompanying documentation, enabling users to access additional information where required.
Access the AHEAD Data Catalogue
Together with the DataLumos archive, AHEAD addresses two of the biggest barriers to reuse of early-grade assessment data: access and interoperability. DataLumos ensures that valuable assessment datasets remain publicly available, while AHEAD provides the harmonised structure, documentation, and metadata needed to analyse them efficiently across countries and over time.
The Road Ahead
AHEAD is an evolving research infrastructure designed to make Africa's early-grade assessment evidence more discoverable, interoperable, and reusable over the long term.
Its underlying framework is guided by four core principles:
- Scalability — enabling future surveys and variables to be incorporated.
- Transparency — documenting every harmonisation decision.
- Traceability — preserving links back to original data sources.
- Analytic flexibility — supporting cross-sectional, longitudinal, and comparative analyses.
Future releases will continue to expand the collection by incorporating additional national and international early-grade assessment programmes, ensuring that AHEAD grows alongside Africa's foundational learning evidence base.
In line with DataFirst's broader purpose of ensuring that every investment in African data creates lasting value, AFLEARN believes that the value of assessment data extends far beyond the projects that originally produced them. By making foundational learning data more Findable, Accessible, Interoperable, and Reusable (FAIR), AHEAD helps ensure that existing investments continue to generate new knowledge for researchers, policymakers, and practitioners working to improve learning outcomes across Africa.
As the collection expands, AHEAD will continue to strengthen Africa's foundational learning evidence ecosystem by making high-quality assessment data easier to discover, compare, and reuse. This will allow researchers to spend less time preparing data and more time generating the evidence needed to improve learning for every child.
Contribute Data: Help Shape the Future of AHEAD
AHEAD is designed to grow. We are continually expanding the collection to include additional early-grade assessment datasets from across Africa.
We welcome contributions from researchers, governments, development partners, and organisations working in foundational learning. We are particularly interested in datasets that are:
- Nationally representative
- Recently collected
- Include longitudinal follow-up of learners or schools
- Feature extended assessment formats (such as three-minute oral reading fluency)
- Include both EGRA and EGMA modules
Together, we can build a richer, more comprehensive evidence base for foundational learning across Africa.
Every new assessment represents a substantial investment of public resources. AHEAD is designed to maximise the long-term value of those investments by ensuring that data remain discoverable, comparable, and reusable for future generations of researchers, policymakers, and practitioners working to improve foundational learning across Africa.