Scholarship Award Recommendation System Using Predictive Modeling and Ensemble Learning

Education is crucial for personal and economic growth, but financial challenges in developing countries hinder equitable academic success. NGOs administer scholarship programs to empower underprivileged individuals, a crucial step towards the attainment of Sustainable Development Goal 4, which aims to provide inclusive and equitable quality education for all. This study proposes a novel Scholarship Award Recommendation System that leverages predictive modelling and ensemble learning to identify deserving students for scholarship awards. The system utilizes a robust ensemble model that combines the strengths of Quadratic Discriminant Analysis (QDA), Random Forest (RF), and Extra Trees (ET) to predict students' academic performance. Additionally, we incorporate answers from the General Mental Health Questionnaire (GHQ-12). The GHQ-12 responses are pre-processed using a binary scoring approach (0-0-1-1) and integrated as predictive variables alongside academic and demographic features. We apply this framework to a case study of Nigerian university students in partnership with Springtime Development Foundation. The results indicate that incorporating GHQ-12 features significantly enhances prediction accuracy, with QDA, RF, and ET achieving accuracy scores of 0.90, 0.86, and 0.89, respectively. Statistical analysis using a t-test confirms the relevance of GHQ-12 features, with a p-value of 0.0013 establishing a significant correlation between student performance and mental health status. The study showed the effectiveness of the ensemble model to accurately predict students’ academic performance. It highlights the significance of incorporating variables from the (GHQ-12) into the predictive model, indicating mental health as a crucial factor for predicting academic performance which in turn enhances the performances of the Classification Models considered.