Student performance prediction is a core task in educational data mining, as it enables early intervention, personalized learning support, and data-driven decision-making. Although existing machine learning models have shown promising results in this domain, challenges persist due to hard-to-classify samples—particularly students exhibiting borderline performance—and the discrete nature of hard labels, which together limit predictive effectiveness. To overcome these limitations, this paper proposes a KFWAdaBoost-based soft label learning framework that systematically enhances baseline model performance through a two-stage synergistic mechanism. In the first stage, K-means++ clustering is... More >