AFLEARN runs a range of capacity-building courses designed to strengthen the use of foundational learning data across Africa. We have begun sharing materials from selected courses to support continued learning and wider access to these resources.
Please note that these materials may be updated over time. If you use these materials in your own work, please acknowledge AFLEARN through an appropriate citation.
We would also love to hear about your experience using these resources. Connect with us on LinkedIn or contact us with any questions or feedback.
Materials are available for both the IRT Literacy and Applied IRT courses. Participants who are new to psychometrics are encouraged to begin with the IRT Literacy course before progressing to the Applied IRT course.
IRT Literacy
This short online course was designed for education professionals who worked with data but had limited formal training in educational measurement. The course built conceptual literacy in Item Response Theory (IRT) and foundational psychometric reasoning without requiring advanced mathematics.
Participants explored how psychometric tools are developed, how they influence fairness and comparability, and how they can be applied responsibly within African educational contexts. Through a combination of theory, reflection, collaborative activities, and interpretation of assessment outputs, participants developed a strong conceptual understanding of IRT and were prepared for more advanced applied training using real assessment datasets.
Applied IRT
Building on the concepts introduced in the prerequisite IRT Literacy course, this residential applied course moved from understanding to practice.
Participants developed practical skills in applying Item Response Theory to large-scale educational assessment data commonly used across Africa. The course introduced the complete applied workflow—from preparing and evaluating data, to selecting and fitting appropriate models in R, to interpreting results for research and policy applications.
Through guided coding, collaborative problem-solving, and a final applied project, participants gained the knowledge and skills needed to conduct and critically evaluate IRT analyses in their own research and policy work.
Closed
This one-week residential course focused on maximizing the impact of foundational learning data. The five-day programme equipped participants with the skills needed to interpret and communicate foundational learning data effectively.
Key topics included:
- Developing strategic communication plans for evidence-informed policymaking
- Interpreting education data while recognising uncertainty and common analytical pitfalls
- Designing clear and effective data visualisations
- Writing concise policy briefs and social media content for non-technical audiences
- Translating technical findings into accessible, evidence-based messages for decision-makers.
Closed
This five-day residential course developed participants' research skills and practical experience in using education data to evaluate policy questions. This course was specially designed to support the 2026 AFEP Scholars, selected Yidan Scholars, and AFLEARN Scholar Residents.
Through lectures, hands-on data analysis, small group work, and case studies, participants strengthened their ability to conduct high-quality applied policy research.
Participants learned to:
- Understand how different types of education data can inform policy research
- Conduct quantitative analyses using appropriate methods and tools
- Situate data analysis within the broader research process
- Interpret and communicate findings for policy audiences
- Build collaborative networks with education policy researchers from across Africa.
Closed
This online course was designed for analysts and researchers working with household survey data on children's foundational learning. Throughout the course, participants worked with the ICAN–ICAR 2025 household survey, a nationally representative dataset covering children's foundational reading and numeracy across multiple African countries.
The course followed a practical, cumulative approach in which each module built on the previous one through guided examples, independent exercises, and reusable R code. A central focus was survey-aware analysis. Participants learned how to account for the complex survey design—including stratification, clustering, and sampling weights—to produce population-representative estimates and appropriate standard errors using R.
The course also emphasised reproducible research practices, including organising projects in RStudio, writing clear and reusable scripts, developing efficient analytical workflows using the tidyverse, and producing publication-ready tables and figures.
Closed