ABSTRACT
Teachers must foresee their students’ performance tendencies to enhance their teaching abilities. This paper discusses advancements and problems in student performance prediction (SPP) and advances in individualised education. It has developed into a helpful resource for various goals; for instance, a strategic plan may be used to establish a high- quality educational system. This study aims to demonstrate how data mining methods may forecast students’ final grades using their past data. This review divides the process of predicting student performance into five stages: data gathering, issue formulation, model development, prediction, and implementation. We performed tests using a data set from the institution and public data established to understand these involved methodologies. The educational dataset, which includes 2500 students, was compiled using an information system representative of a regular university. Finally, existing deficiencies and intriguing future studies are outlined based on the experimental findings from collecting data.
