MODELING THE EFFECTIVENESS OF FPV DRONE OPERATOR TRAINING USING SIMULATORS AND ONLINE PLATFORMS

Abstract

This article examines the key conditions and factors influencing the training efficiency of FPV drone operators through simulators and online platforms in Kazakhstan. The study aims to address the lack of standardized methodologies and national frameworks for UAV operator training by identifying socio-economic, technical, and pedagogical determinants that shape learning outcomes. Using a mixed-method approach combining literature analysis, comparative assessment of international practices (USA, China, the UK, and Australia), and mathematical modeling, the research formalizes the relationship between simulator-based learning, real flight practice, and external factors. The proposed integrated model E(t) quantifies training efficiency as a dynamic function of simulation-based skill acquisition, reinforcement through practical flights, and the impact of organizational and infrastructural conditions. Results demonstrate that hybrid training pathways – combining intensive simulator preparation with supervised real flights – significantly enhance skill retention and operational safety while reducing costs and training time. Comparative analysis of global ecosystems reveals that advanced training systems increasingly integrate virtual and augmented reality (VR/AR) and artificial intelligence (AI) for adaptive learning and error analytics, whereas Kazakhstan faces challenges of uneven infrastructure development and limited access to standardized resources. The findings underscore the need for national adaptation of international best practices, the creation of domestic simulation centers, and the development of unified educational standards for FPV operator certification. The proposed model and recommendations can serve as a foundation for policy development, simulator design, and academic curricula, contributing to the formation of a skilled workforce and the sustainable growth of the national drone industry.