Its datasets are drawn from anonymised, aggregated platform data across 2,300+ schools in 11 countries, alongside survey and interview work with school leaders and teachers. Methodology and limitations are published with every release.
Why Edves publishes research
Education technology is unusually well supplied with claims and unusually poorly supplied with data. Most published figures in the sector are either vendor marketing or survey work at a level of abstraction too high to act on. Edves operates schools’ day-to-day systems in eleven countries, which puts it in an uncommon position: it can observe what actually happens to attendance, assessment participation, teacher planning time and digital adoption at scale.
The commitment attached to that is straightforward. Every release states its method, its sample, and what it cannot show.
Research programme
Digital adoption and readiness
- Digital adoption patterns in African schools — what schools actually use once they have a platform, as against what they buy.
- AI readiness in schools — infrastructure, staff capability, policy maturity and leadership posture.
- Connectivity and device access as a determinant of platform value.
Teaching and teacher development
- Teacher planning and marking time before and after AI-assisted workflows.
- What observation feedback actually contains, and whether evidence-anchored rubrics change it.
- CPD engagement patterns across school types and systems.
Assessment and attainment
- Formative assessment frequency and its relationship to terminal examination performance.
- Assessment integrity in the generative AI era.
- Subgroup attainment reporting and what disaggregation reveals that averages hide.
Attendance and engagement
- Attendance patterns and early-warning signal reliability.
- Parent communication channel effectiveness by market and channel.
- Chronic absence: what precedes it and how early it is detectable.
Planned publications
| Publication | Scope | Cadence |
|---|---|---|
| State of Digital Education in Africa | Adoption, connectivity, digital capability and platform use across African markets | Annual |
| AI Readiness Index for Schools | Infrastructure, capability, policy and leadership readiness, scored and benchmarked | Annual |
| School Digital Transformation Index | Depth of digital adoption across administration, teaching and assessment | Annual |
| Assessment in the Age of AI | How assessment design is changing across K–12 and higher education | Periodic |
Method and limitations
Platform-derived data has a specific and important limitation: it describes schools that have already adopted a digital platform. It cannot be read as representative of all schools in a country, and Edves does not present it that way. Where a finding is drawn from a subset — a single market, a pilot programme, a school type — the subset is named.
All platform data used in research is anonymised and aggregated. No individual student, teacher or school is identifiable in any published output. Schools participating in named case studies do so by agreement.
Citing Edves Research
Publications are intended to be citable. Each release carries a publication date, named authors, a stated method, and a version. Where a figure is revised, the revision is recorded rather than silently replaced. For company-level figures rather than research findings, cite the Edves facts page.
Working with us
Edves collaborates with universities, ministries and research institutions on studies that require operating-school data. The Centre for Experiential Learning & Innovation partnership in Enugu State, Nigeria, is one example of research embedded in a live system-level reform programme. Enquiries through the contact page.