Aerospace Technic and Technology

The subject matter of the article is the processes of self-directed learning (SDL) skill formation for air traffic controllers at the pre-simulator training stage to acquire competence for the purposeful mastery of their professional knowledge, skills, and abilities, as well as the ability for self-organization, personal, and professional self-improvement, which will contribute to their successful professional development and continuous professional development throughout their lives. The existing SDL sub-competencies will enable the air traffic controller to use them in the simulator training in self-regulation mode. The article develops an adaptive SDL method for an air traffic controller in the pre-simulator training stage. The tasks: to describe the basic principles of SDL for air traffic controllers; to develop a cyclical SDL model for pre-simulator training of air traffic controllers; to build a conceptual model of the system of air traffic controller pre-simulator training based on SDL and an adaptation model as the main component of the automated system of adaptive pre-simulator training of air traffic controllers. The methods used are analysis, comparison, and synthesis; system analysis; graphic and object modeling; decomposition methods and graph theory. The following results were obtained. The cyclic model of SDL presented in the paper is the basis for the developed conceptual model of the system of air traffic controller pre-simulator training, based on the principles and elements of SDL. The considered adaptation model can be applied in the automated system of pre-simulator training of air traffic controllers. Conclusions. The scientific novelty of the results obtained is as follows: the proposed approach to the adaptive pre-simulator training of air traffic controllers will optimize the process of such training not only in sense of improving the quality of the training itself in the formation of professional competencies but also in terms of developing special cognitive and metacognitive skills, making the pre-simulator training itself indeed signs of adaptation, considering the individual preferences of the student in the strategic and tactical management of their learning.

adaptive learning; air traffic controller; pre-simulation training; self-regulation; self-directed learning; adaptive model; automated system

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