Mathematical Algorithms for Educational Data Classification addresses the critical need for rigorous and transparent methods in educational assessment, where machine learning approaches often lack interpretability despite influencing important educational decisions.
The book bridges advanced mathematical theory with practical application by demonstrating how variational inclusion problems, equilibrium formulations, and iterative algorithms can be applied to educational data classification with provable stability and convergence. Organized into three parts—foundational concepts, iterative algorithms with convergence analysis, and practical case studies—it emphasizes both algorithmic construction and interpretable, equitable results in real educational contexts.
Designed for graduate students, researchers, and professionals in mathematics, learning analytics, and mathematics education, this book serves those seeking both theoretical rigor and actionable methodologies to develop more transparent, equitable, and evidence-based educational decision-making systems.