Prediction and Comparative Analysis of Factors Affecting the Mathematical Achievement of Gifted Students With Machine Learning Models

This study aims to determine the factors affecting the mathematical achievement of gifted students studying at science and art centers in Bursa province and to predict this achievement using various machine learning models. In the study, variables, such as demographic information, family structure, study habits, motivation level, technology use, and social activities were analyzed in line with the data collected from 151 students. Methods, such as decision trees, support vector machines, and artificial neural networks, were used by utilizing the fields of educational data mining and learning analytics. The results obtained showed that some variables significantly affected the mathematical achievement of students. The study provides important findings in terms of developing educational policies and individualized teaching strategies.