<![CDATA[This study investigates the application of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing multiple conflicting objectives related to student academic performance. Using the Student Performance dataset from the UCI Machine Learning Repository, which contains demographic, behavioral, and academic information of 395 secondary school students, the research aimed to maximize final grades (G3), minimize absenteeism, and maximize study time. The study began with exploratory data analysis, which revealed wide variability in academic outcomes, low average absenteeism, and moderate study time, justifying the selection of these three objectives. NSGA-II was then implemented with a population of 100 individuals across 200 generations, employing crossover and mutation operators to generate Pareto-optimal solutions. The results demonstrated diverse non-dominated solutions, illustrating trade-offs between academic achievement, attendance, and study time. Absenteeism emerged as the most significant negative factor, while study time and school support were positively associated with better outcomes. Unlike conventional regression or classification methods that produce a single prediction, NSGA-II provided a spectrum of optimal alternatives, offering flexibility in policy and decision-making. These findings highlight the relevance of multi-objective optimization in education and emphasize the importance of integrating behavioral, social, and digital dimensions to design adaptive strategies for improving student performance.]]>
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