Educational data mining: a 10-year review
Emi Kalita, Solomon Sunday Oyelere, Silvia Gaftandzhıeva, Kandala N. V. P. S. Rajesh, Senthil Kumar Jagatheesaperumal, Asmaa Mohamed, Yomna M. Elbarawy, Abeer S. Desuky, Sadiq Hussain, Mehmet Akif Çifçi, Paraskevi Theodorou, Slavoljub Hilĉenko, Jiten Hazarika, Tazid Ali
Abstract
Abstract This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations.