From a systems perspective, OppiaMobile combines:

• an offline‑first Android client
• a server‑side content and analytics backend
• integration with Moodle for course authoring and content management.

Courses are authored in Moodle and exported to the OppiaMobile server, where they are packaged and distributed to mobile devices. Learners download courses to their devices, complete activities offline (videos, quizzes, interactive modules), and later synchronise results and usage data back to the server. This enables large‑scale training programmes to operate reliably in settings with intermittent connectivity.

Key technical features include offline data storage, incremental synchronisation, learner progress tracking, assessment scoring, and analytics on content engagement and completion. The platform also supports multilingual content, text‑to‑speech, and gamified learning elements such as points and badges.

I acted as the originator, product owner, and technical lead for OppiaMobile over an extended period, with responsibility for defining the system architecture, setting the product roadmap, and leading development across client and server components. I remained closely involved as the platform evolved from pilot deployments into national‑scale implementations.

A significant aspect of the work involved designing and refining analytics and monitoring workflows, allowing programme managers and health authorities to track learner engagement, completion rates, and assessment outcomes across large, distributed user bases. These analytics were used to iteratively improve both platform features and training programme design.

Following the initial pilot, the platform evolved into a production system used at national scale in Ethiopia, including deployment during the COVID‑19 response, where it reached approximately 20,000 users within the first day of launch. I worked directly with government partners, NGOs, and regional health authorities to adapt the platform to operational and policy requirements in each context. Additional deployments and pilots have been delivered in collaboration with government bodies and partners in Liberia, Uganda, India, Pakistan, Zambia and the Philippines.

Although OppiaMobile does not incorporate machine learning directly, the project demonstrates long‑term ownership of a production‑grade, data‑driven system operating at national scale. It provides strong evidence of the ability to design, deploy, and sustain complex technical platforms in real‑world healthcare settings - combining mobile development, backend services, analytics, open‑source governance, and close collaboration with public‑sector stakeholders.

Code & Artifacts