Personalizing the learning process through data mining in higher education

Abstract

Relevance. The rapid evolution of the modern labor market necessitates a shift from traditional, standardized career counseling to personalized educational trajectories that account for individual qualities, skills, and preferences, ensuring better professional adaptability and satisfaction. Purpose. The purpose of the study is to develop and validate a model that will facilitate the selection of a person-centred educational and professional pathway for students. Methodology. The research methods are comparative, qualitative, and statistical analysis, experimental method, modelling, and prototyping. Results. The main result of the study was the creation and successful application of an original learning model integrated with modern analytical approaches, including predictive analysis mechanisms for adapting and optimizing individual educational trajectories, which significantly improved the efficiency of the learning process. Experimental implementation showed that students who used the developed model to create their individual educational trajectories achieved a significant improvement in their academic performance and motivation to learn compared to the control group. The hypothesis that personalized guidance influences the choice of more appropriate curricula and courses was confirmed, which in turn has a positive effect on academic performance and satisfaction with learning. The presence of positive feedback from students and teachers also indicates the high adaptability of the model and its potential for scaling up and further implementation in educational practice. Conclusions. The study validated the positive impact of a personalized approach on students' academic motivation and performance, revealing significant advantages in engagement, strategic career planning, and long-term purposefulness, while also highlighting the need for further research to scale the model, optimize prediction algorithms, and adapt the approach to diverse learning environments and educational standards. Keywords: adaptive education; statistical modelling; educational modelling; learning technologies; educational motivation; forecasting methodologies; qualification development