Leveraging advanced data mining techniques for personalized education

Abstract

Modern technology in education has introduced personalized learning where the educator can adapt their teaching methods based on what a student can and wants to do. Advanced data mining methods help personalize learning by analysing large datasets and recognizing patterns or trends that can support instructional decision making, thus enhances personalized education effectiveness. This theoretical essay considers how Educational Data Mining (EDM) is relevant to supporting personalized education. The study is based on the literature review approach as it evaluates academic publications on the topic in credible online databases including Scopus, Web of Science, emerald, ScienceDirect and google scholar. The analysis also illustrates how advanced data mining techniques, such as classification, clustering, and machine learning, could be used to assess the large educational datasets in order to determine learning patterns, predict student performance, and aid in adaptive instruction. The discussion indicates that EDM has the potential to enhance student engagement, early intervention of the at-risk learners, and evidenced based teaching methods. Future studies must address ethical concerns, privacy of data and how individualized data-based learning systems can be scaled.