Early-warning Dropout Visualization (EDV) Tool
As a young data scientist, I want to integrate high quality, open data into a tool that can help reduce adolescent girls’ school dropouts and in-return achieve reduction of HIV/AIDS contraction by young women.
Prisila Ishabakaki, Sarah Nyanjara, Neema Mduma, Andrea-Michael Kileo, Himili Mbawala and Janeth Marwa
Decrease early school dropouts that limit opportunities for AGYW
This project will address the problem of data collection, on-time data submission, and effective data visualization from the grassroots (school, village, ward, and district) to higher levels through the use of emerging technologies. By using affordable technologies for statistical visualization and mobile application, the project will facilitate data submission, analysis, and presentation from one level to another. The EDV tool will use mobile and visualization technology to create understandable representations of dropout status. Thereafter, dropout status will be provided to the user through a mobile message or unique beep, helping grassroots entities to act quickly to prevent girls’ dropout. The EDV tool will reduce HIV risk by identifying students at risk of dropping out so that special measures can be taken. The tool will also increase the capacity to use high quality, open data.