Development of principles for training students of oil and gas specialties using machine learning technologies

Abstract

The modern oil and gas industry is experiencing a period of technological transformation that requires a fundamental change in approaches to specialist training. The article presents the results of research on integrating machine learning technologies into the educational process for students in oil and gas majors as a response to the industry's growing demand for personnel proficient in methods of intellectual data analysis. A pedagogical model is proposed, which includes problem-based, project-based, and blended learning, aimed at developing competencies in machine learning and artificial intelligence. Based on an empirical study involving 426 students from 8 specialized universities, it was found that the implementation of the developed model contributes to a 42.3% increase in graduates' readiness to solve professional tasks using intelligent technologies. A positive correlation was established between the degree of integration of machine learning technologies into the educational process and the employment indicators of graduates (r=0.78, p<0.01). The results of multivariate analysis demonstrate that the effectiveness of the proposed model is determined by the synergy of the modular structure of content, the digital educational environment, and practice-oriented methods. The developed set of pedagogical conditions can be adapted for the training of specialists in other engineering fields and used in the design of educational programs in the conditions of the digital transformation of education.