How to Build a Reader : Evidence from a Scalable Literacy Intervention in Ghana

Overview & Synopsis

Addressing the massive test score gaps between rich and poor countries will require programs that are both high-impact and scalable. This study uses the results of a randomized controlled trial in low-fee private schools in Ghana to study a program that meets both needs. The Tools for Foundational Learning Improvement program increased test scores by 0.5 standard deviation after just nine months of intervention. A machine learning method decomposes the effects by predicted test scores if the students did not receive the treatment, and the findings show that the gains were larger for weaker students. Moreover, the program’s impacts scale roughly linearly with time compared to a shorter-term, smaller-scale pilot randomized controlled trial. The program’s developers used generative artificial intelligence to accelerate lesson plan development and adaptation to new settings. An observational pilot test of this adaptation in Uganda yielded comparable results to those of this study’s randomized controlled trial. The study developed a model in which basic skills constrain the development of advanced skills, which predicts the pattern of effects observed across early reading capabilities, and it makes forecasts about the future impacts of the program as it continues into second grade.