Student Academic Prediction Using Machine Learning
Keywords:
Academic Prediction, Educational Sociology, Machine Learning, Early Warning Systems, Learning Analytics, Predictive Ethics.Abstract
The trajectory of a student’s academic journey is rarely linear, often shaped by an intricate mosaic of social, economic, and personal variables. Early prediction of academic
performance has emerged as a vital mechanism for educational institutions seeking to provide timely, empathetic, and targeted academic support. This paper presents a comprehensive, socio-technical machine learning framework designed to forecast student performance using the publicly available UCI Student Performance dataset. Recognizing that mathematical
competence often serves as a critical benchmark for broader academic resilience, we focus specifically on the Mathematics subset. This subset provides a reproducible, highly contextualized view into demographic, social, study-behavior, and institutional variables. Our workflow transcends mere algorithmic execution, integrating data acquisition, sensitive
preprocessing, exploratory social data analysis, feature nuance analysis, and ethical target construction. We consider a suite of candidate classifiers—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—evaluating them not merely on sterile accuracy, but on their real-world utility in an early-warning context. The final-period grade (G3) serves as the primary academic outcome, which we operationalize into actionable pass/fail categories using the conventional pedagogical threshold of 10 on a 20-point scale. A significant methodological discourse within this study surrounds the ethical and
practical implications of utilizing intermediate grades (G1 and G2). As historical performance strongly dictates future outcomes, predicting without them poses a greater mathematical
challenge but offers profound utility for genuinely early intervention. By framing these models as decision-support systems rather than deterministic oracles, this framework advocates for the responsible use of predictive analytics—prioritizing privacy, fairness, and the human element
inherent in educational support.
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