Аңдатпа
The growing intricacy of IT education requires resources to aid students in choosing specialized pathways. This study investigates the prediction of specialization preferences among IT students at SDU University in Kazakhstan through the application of machine learning techniques. The research contribution is the development of a predictive model that enhances academic advising by incorporating multiple factors, including academic performance, personality traits, qualifications, and extracurricular involvement. The research examined 692 anonymized student profiles and evaluated the efficacy of five machine learning algorithms: Random Forest, K-Nearest Neighbors, Support Vector Machine, Gradient Boosting, and Naive Bayes. Stratified 10-fold cross-validation was utilized to reduce the risk of overfitting. Gradient Boosting attained a peak accuracy of 99.10% in validation; however, its performance decreased to 92.16% on an independent test set, suggesting overfitting. Naive Bayes exhibited the lowest accuracy, recorded at 35.26%. Logistic regression analysis indicated a statistically significant correlation (p < 0.05) among academic performance, extracurricular involvement, and specialization selection. Personality traits and certifications significantly influenced the prediction process. The findings suggest that although Gradient Boosting demonstrates high effectiveness, the associated risk of overfitting requires additional refinement for practical application. The notable impact of academic performance and extracurricular activities indicates that educational institutions ought to prioritize these elements in student guidance. The incorporation of machine learning-based recommendations into advising frameworks enhances the precision of specialization predictions, thereby improving student decision-making and career alignment.