Predictive Machine Learning-Driven Framework for Student Performance and Dropout in Online Learning Under Insurgency

Abstract

This study presents a context-aware Machine Learning (ML) framework for predicting student performance and dropout in online learning environments affected by insurgency-related disruptions. Unlike conventional models that assume stable learning conditions, the proposed framework explicitly integrates environmental instability into its predictive architecture. The system combines assessment records, institutional data, Learning Management System (LMS) logs, and survey-derived contextual indicators within a unified analytical pipeline. Main variables include forum participation, login frequency, time-on-task, submission regularity, and disruption sensitive behavioral patterns linked to insecurity and displacement. To improve reliability under unstable conditions, the framework incorporates normalization, missing-value imputation, temporal alignment, and imbalance handling during preprocessing. Prediction is performed using Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine models to enable robust comparative learning. A multi-objective optimization strategy balances predictive accuracy, fairness, robustness, and interpretability, while SHAP-based explainable AI enhances transparency in decision-making. The framework advances learning analytics through disruption aware predictive intelligence for early identification of at-risk learners and supports resilient, adaptive, and equitable online education in crisis-affected environments.