Mathematical Algorithms for Educational Data Classification

1. Auflage 2026
ISBN: 978-1-041-29047-6
Verlag: Taylor & Francis Ltd

Buch, Englisch, 256 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Mathematics and its Applications

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.

Zielgruppe

Professional Reference

Autoren/Hrsg.

Jun-on, Nipa

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik Mathematik Operations Research

Inhaltsverzeichnis

Part 1: Foundations of Educational Data Classification. 1. Mathematical Foundations. 2. Variational Inclusion Problems. 3. Equilibrium Problems (EP). Part 2: Algorithms for Data Classification. 4. Iterative Algorithms. 5. Advanced Algorithmic Techniques. 6. Algorithm Convergence. Part 3: Applications in Educational Data. 7. Classifying Teacher Competencies. 8. Student Skill Prediction. 9. Digital Proficiency in Education.

Über Autor(innen)

Dr. Nipa Jun-on serves as an Associate Professor in the Department of Mathematics at Lampang Rajabhat University, Thailand, where she specializes in bridging mathematics and mathematics education. Her innovative research centers on leveraging advanced mathematical algorithms to predict and enhance both teacher competencies and student performance in mathematics. With more than a decade of experience in teacher education, Dr. Jun-on has pioneered groundbreaking applications of fixed-point theory and variational methods for educational data classification within the Thai educational context. She is deeply committed to evidence-based educational policy and has collaborated extensively with teacher preparation programs to strengthen technology integration and advance digital proficiency among pre-service mathematics teachers. Dr. Jun-on's work represents a unique fusion of theoretical mathematics and practical educational applications, positioning her at the forefront of data-driven approaches to mathematics education reform.