Student elective course selection patterns and satisfaction determinants identified through educational data mining

Digital transformation in higher education is reshaping how institutions design and deliver their curricula, with a growing emphasis on student agency and personalized learning paths. This study employs educational data mining techniques to analyze student preferences and satisfaction with elective courses at Kryvyi Rih State Pedagogical University in Ukraine. We investigate patterns in course selection, satisfaction determinants, and the effectiveness of the university's individual educational trajectory framework among 1,089 students. Our analysis reveals four distinct student segments with varying preferences and satisfaction profiles. Information availability before selection, alignment with career goals, teaching quality, and course relevance emerge as significant predictors of student satisfaction. We propose a data-driven framework for optimizing elective course systems that incorporates learning analytics, personalized recommendation engines, and enhanced information platforms. This research contributes to understanding how educational technology can better support student agency in curriculum customization while addressing critical issues of accessibility, equity, and educational quality. The findings align with Sustainable Development Goal 4 (Quality Education) by promoting inclusive and personalized educational opportunities that prepare students for future employment challenges.