Machine Learning Model for Predicting Student Dropout: A Case of Tanzania, Kenya and Uganda

Abstract

Student dropout is among the challenges that face most schools in developing countries particularly in Africa. In addressing the student dropout problem, a thorough understanding of the fundamental causative factors is essential. Several researchers have identified and proposed causes, methods and strategies that will help to reduce or stop the student dropout problem, however, most of the proposed solutions did not show promising results and the dropout trend continue to increase over time. Machine learning on the other hand has gained much attention when addressing society’s problems in different sectors including education. This is attributed by the fact that, machine learning models when accurately trained, provide convenient and reliable results as compared to the traditional approaches. This study focused on developing a machine learning model that will help to predict and identify students who are at risk of dropping out of school. Three datasets from Tanzania, Kenya and Uganda were used to develop the model and disclose the best classifier from the three commonly used i.e. Multilayer Perceptron, Logistic Regression and Random Forest. Classifiers were evaluated using Geometric Mean and F-measure to examine their performance. Results revealed that, Logistic Regression achieved the highest performance as compared to the other two. The study, therefore, recommends the developed model to be used by relevant authorities in identifying and predicting students who are at risk of dropping out of schools, and make informative decisions on addressing the student dropout problem.