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Many girls in Mozambique face discrimination, gender-based violence, and poverty. The country has the fifth highest rate of child marriage in the world and its pregnancy rate is the highest in East and Southern Africa, 180 out of 1000 aged 15 and 19 gave birth in 2023, in contrast with the regional average of 94 birth per 100 girls.
Considering the education marginalisation and intersection of multiple factors, VSO implemented an integrated development project-Empowering Adolescents and Girls to Learn and Earn (EAGLE) to support 4,328 (15-19 years old) vulnerable, girls who dropped out of school and young women, specifically those with children, living with disabilities and/or affected by HIV/AIDS and an orphan in Mozambique between 2020 to 2025. Key areas of interventions included: accelerated education classes and life skills focusing on Sexual Reproductive Health Rights education by using tablet based Edtech solutions, livelihood and income generation skills, financial literacy, behavioural change, safeguarding and protection and male engagement. Through out the project, The endline evaluation used qualitative and quantitative data (attendance, saving etc) collected by VSO through the project cycle. At the end of the project, 94% of girls enrolled in the EAGLE project passed the Literacy National Education Exam in comparison to 86.7% nationally. The final cohort in Manica (1177) managed a pass rate of 99.75%. 72.4% of girls surveyed understand family planning, contraception, and safe motherhood. 73% of girls can now save money.
Drawing on qualitative and quantitative data from the midterm evaluation, this paper answer to the following questions:
How did EAGLE project identify and address intersectional inequalities while fostering innovation and context-specific approaches?
How did the project successfully address and challenge the intersectionality of social norms to enable vulnerable girls and adolescents to access to, participation in education and economic empowerment?
What challenges did the project face while applying inclusive and participatory data methodologies address challenges related to data disaggregation, definitional challenges, and intersectionality?
What lessons can we learn using Edtech solutions in low-income settings?