Predictive Modeling of Students’ Dropout Risk Using Intelligent Analytics (Published)
Student dropout is a persistent challenge in higher education, particularly in developing countries like Nigeria, where reactive institutional responses often fail to identify students at-risk in time. This study proposes an intelligent analytics-based predictive modeling framework designed to transition institutional strategies from reactive to proactive early intervention. Using a dataset of 2,200 student records from Federal Polytechnic Ukana and Akwa Ibom State Polytechnic, the research evaluates the effectiveness of two ensemble learning algorithms: Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The methodology involved robust data preprocessing, including Min-Max normalization and Principal Component Analysis (PCA), which identified 16 key predictors from an initial 22 variables. These variables spanned academic performance, demographic backgrounds, and behavioral patterns. Experimental results conducted in a Python environment revealed that XGBoost outperformed RF across all evaluation metrics. XGBoost achieved an accuracy of 0.92, precision of 0.91, recall of 0.90, and an F1-score of 0.91, compared to RF’s accuracy of 0.87. Feature importance analysis highlighted “Attendance in Classes” and “Previous Academic Results” as the most significant predictors of attrition. The study concludes that intelligent analytics can effectively capture nonlinear relationships in student data to provide actionable insights. This framework offers a scalable solution for Nigerian tertiary institutions to implement evidence-based retention strategies, ultimately improving graduation outputs and institutional efficiency.
Keywords: Nigerian higher education, Random Forest, XGboost, educational data mining., intelligent analytics, predictive modeling, student dropout risk