ENHANCING STUDENT PERFORMANCE THROUGH DATA ANALYTICS PREDICTION

Abstract: It is becoming more and more important to comprehend and maximise student achievement in the quickly changing academic environment of today. In order to solve this problem, educational institutions are using data analytics more and more to put in place predictive algorithms that can recognise students who are at-risk and adjust treatments accordingly, leading to improved retention rates and better academic results overall. Specifically, the application of machine learning algorithms has demonstrated potential in the analysis of diverse student-related data, including demographics, academic background, and behavioural tendencies, for the purpose of predicting performance trajectories and creating customised support plans for individual students. Furthermore, studies have demonstrated that automated systems capable of real-time prediction of student performance can enhance decision-making processes regarding timely educational interventions, thereby fostering greater engagement and motivation among students. Moreover, the integration of these predictive models not only aids in early identification of students who may require additional support but also provides institutions with insights to refine their educational practices and resource allocation, ultimately creating an effective and responsive environment for learning. Through the integration of multiple variables, including socioeconomic status, time management proficiency, and involvement with educational materials, educational institutions can create advanced predictive models that greatly improve their capacity to assist students who may be at risk of performing inadequately or halting their studies. Keywords: Big data, types of analytics, higher education, prediction