INTERNATIONAL JOURNAL FOR INNOVATIVE RESEARCH IN MULTIDISCIPLINARY FIELD

Abstract: Data mining serves as a powerful approach for extracting meaningful insights and uncovering significant relationships among variables contained within large datasets or data warehouses. In this study, the clustering algorithm, a well-known data mining technique, is employed to analyse students’ learning behaviours. The method is utilized to derive actionable knowledge from the educational environment. Various student evaluation parameters—including previous semester marks, midsem, endsem marks and attendance—are examined in this process. It is suggested that the correlated outcomes obtained from these analyses be communicated to course instructors prior to the administration of the final examination. The findings of this research are expected to assist educators in reducing student dropout rates and enhancing overall academic performance.