Аннотации:
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, KNearest 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.
