Literacy is transformative as it can increase earning potential, decrease inequality, improve health outcomes and break the cycle of poverty. In Sub-Saharan Africa (SSA), at least 1 in 3 adults cannot read. Youth literacy rate in SSA, 72%, is the lowest of any region and there are many millions of children and youths that do not attend school. Youth engagement in reading and learning can be improved through the gamification of literacy and by using Information and Communication Technologies, namely mobile phones, which can take an important role in tracking literacy and in motivating people to learn how to read and write.
Several techniques for assessing literacy can be used: the traditional oral reading fluency tests, the Test of Written Spelling, lexile measurements, the early grade reading assessment, among others. Machine learning techniques can simultaneously be used to identify user literacy level specific behaviours during the literacy assessment. These behaviours can be identified, and analysed, helping to define user profiles and automatically adapt smartphone applications’ interfaces for each user.
Author: Marcos Oliveira
Type: MSc thesis
Partner: Faculdade de Engenharia da Universidade do Porto