An Effective Optimization in Education System using Decision Support Systems

Abstract

For academics, the process of retrieving information from large datasets, known as data mining, has become a captivating area of research. The concept of utilizing data mining techniques to extract information has been in existence for several decades. The dataset was initially designed to be divided into sections and analyzed using classification and clustering methods to explore its intrinsic characteristics. They make their forecasts based on these features. These predictions have been generated in the field of educational data mining for several purposes, such as forecasting student achievement using individual traits and assisting students in identifying suitable professors and courses. These targets have been derived from the analysis of student attrition and retention. Our study is centered around the aims of student attrition and retention. In addition, we have discovered intriguing indicators that contribute to the prediction of students' success, indicating the most competent instructors, and helping them with their choice of courses.