MULTIPLE REGRESSION ANALYSIS AS A TOOL FOR ACADEMIC ACHIEVEMENT PREDICTION

Abstract

Academic achievement is one of the most important indicators used to evaluate student learning outcomes, educational effectiveness, and institutional performance. Educational institutions increasingly rely on statistical methods and data analytics to understand the factors influencing student performance and to develop strategies that enhance academic success. Predicting academic achievement has become a major area of interest in educational research because accurate prediction models can assist educators, administrators, and policymakers in identifying at-risk students, allocating resources effectively, and improving educational planning. Various demographic, psychological, socioeconomic, and academic factors contribute to student performance, making academic achievement prediction a complex analytical problem. Among the available statistical techniques, multiple regression analysis has emerged as one of the most widely used methods for predicting academic performance. Multiple regression enables researchers to examine the simultaneous effects of several independent variables on a dependent variable and quantify the relative contribution of each predictor. In educational contexts, variables such as attendance, study habits, parental education, socioeconomic status, prior academic performance, motivation, and classroom engagement are frequently analyzed to predict academic achievement. The ability of multiple regression models to identify significant predictors and generate reliable forecasts makes them valuable tools for educational assessment and decision-making. This study investigates the application of multiple regression analysis as a tool for academic achievement prediction. The research aims to identify key factors influencing student performance and develop a predictive model capable of estimating academic achievement based on selected explanatory variables. The study employs quantitative research methods and utilizes statistical techniques including descriptive statistics, correlation analysis, multiple regression analysis, ANOVA, and hypothesis testing to examine relationships among variables and assess model effectiveness. The findings are expected to demonstrate that multiple regression analysis provides a reliable framework for understanding and predicting academic achievement. Variables such as attendance, study hours, previous academic performance, and parental support are anticipated to exhibit significant relationships with student outcomes. The study contributes to the field of educational statistics by highlighting the practical value of predictive modeling in academic environments. Furthermore, the research offers insights that can assist educators in implementing evidence-based interventions designed to improve student achievement and educational quality. As educational institutions increasingly adopt data-driven decision-making approaches, statistical prediction models will continue to play a crucial role in supporting academic success and institutional effectiveness.