Comparative Study of AutoML Approach, Conventional Ensemble Learning Method, and KNearest Oracle-AutoML Model for Predicting Student Dropouts in Sub-Saharan African Countries

Abstract

Student dropout in secondary schools is a major issue in developing countries, particularly in Sub-Saharan Africa. Sub-Saharan African countries had the highest dropout rate (37.5%), followed by South Asia (15.5%), the Middle East (11%), East Asia (9.5%), Latin America (7%), and Central Asia (3.5%). Various initiatives such as the big results now initiatives, no child left behind, and secondary education development programme as well as machine learning prediction models have been used to reduce the severity of the problem in Sub-Saharan countries. The ongoing dropout problem, particularly in secondary schools is ascribed to improper root cause identification and the absence of formal procedures that can be used to estimate the severity of the issue. This study has compared the AutoML model, ensemble learning approach, and KNORA-AutoML to predict student dropout problems. The KNORA-AutoML model scored 97% of accuracy, precision = 71%, and AUC = 87% when compared to the conventional ensemble of optimized ML models with accuracy = 96%, precision = 70%, and AUC = 78%. KNORA-AutoML model performance increased by 0.6% accuracy, 0.8% precision, and 8.7% AUC. An optimized model draws a lot of attention to the findings related to student dropout rates in developing countries.

Keywords

• machine learning techniques
• automated machine learning
• hyper-parameter optimization techniques
• student dropout prediction
• secondary school dropout