Rose Peter Funja

Health Related Dropout and Low Literacy Risk Monitor

As a woman and an engineer, I want to use predictive analytics and machine learning to help District Planning Officers predict the impact of education budgets on the school dropout rates among girls so that we can reduce these dropout rates and help girls get an education.

Empowering Citizens

Mbeya and Nzega

Rose Peter Funja, Alfred Elias Kajirunga, Hashimu Rwenyagira Jabil, Hamisi Jumanne Kalegele, Vivian Timoth Wonanji, and Emmanuel Chifuel Manasse

My solution, Dropwall, is an analytical electronic tool that will use a novel indicator known as Dropout and Low literacy Likelihood (DLLI) to help narrow down the target groups to the much riskier girl(s) as regards to school dropout challenge so that interventions can be more focused and timely. The DLLI tool will leverage the ongoing Government push to avail quality data freely, and the increased usage of mobile phones among Tanzanian population.

Scale-up of Dropwall will involve two main aspects (1) improvement in the system’s usability, security and data harvesting; and (2) improvement of the performance of the models and algorithms. We also aim to widen the model to new districts so that more District Officers can benefit from the predictive power of Dropwall.